All posters (850)

The live site filters this list with an interactive "Load more" control that needs its original server. This page lists every poster instead, searchable by title.

• A-B.01: Inferring Population-Level Nutrient Consumption Patterns from Food Consumption Information Data
• A-B.02: Prediction and analysis of new HisKA-like domains
• A-B.03: Explainable Deep Learning for Phage-Host Infection Prediction from Whole Genomes
• A-B.04: Habitat Invasibility Associated with Lantana camara Reveals Change in Bacterial and Fungal Community Structure and Function
• A-B.05: Impact of bioinformatic pipeline selection on microbial community inference: a comparative analysis across pipelines including MetagenApp
• A-B.06: Whole-genome sequencing and comparative biosynthetic analysis of Pleurotus albidus: uncovering secondary metabolite and Beta-glucan biosynthetic potential in a Neotropical edible mushroom
• A-B.07: Extending AnnoTALE for Improved TALE Annotation and Analysis Across Xanthomonas Genomes
• A-B.08: Designing effective dsRNAs for RNAi-based plant protection
• A-B.09: VPF-Class 2.0: a taxonomy-centered framework for automatic viral classification
• A-B.10: Connecting 16S Amplicon Sequencing to Genomic and Ecological Context at Scale via mOTUs-db
• A-B.11: EnHostDB: A curated database of high-quality environmental and host-associated bacterial genomes for comparative genomics and pathogenicity prediction.
• A-B.12: Context-Dependent Mutation Rates and Fitness Landscapes in RNA Viruses
• A-B.13: Effects of Seasonality on the Evolution of Endemic Respiratory Virus Populations
• A-B.14: Developing a new computational framework to quantify the potential impacts of chemicals on the genetic makeup of wild populations
• A-B.15: The Rises and Falls of the Visual Opsin Genes in Two Hundred Cypriniformes Fishes
• A-B.16: Fair Epidemic Mitigation via Multi-Objective Reinforcement Learning
• A-ELIXIR.01: Building a community-driven linked knowledge graph for plant specialized metabolism
• A-ELIXIR.02: LongPolyASE: An end-to-end framework for allele-specific gene and isoform analysis in polyploids using long-read RNA-se
• A-G.01: esloco: simulation-based estimation of local coverage in long-read DNA sequencing
• A-G.02: Analytic Integration over Tree Space: Efficient Inference of Population Dynamics
• A-G.03: Insights into the genomes of Mycobacterium abscessus: Decoding the arsenal shaping pathogenesis
• A-G.04: Comparative Analysis of Sugar Transporters in Yeast Strains for Improved Xylose Utilisation in Biofuel Applications
• A-G.05: ChemGenXplore: an interactive tool for exploring and analysing chemical genomic data
• A-G.06: ROQ: a new measure for more accurately filtering mapped reads
• A-G.07: Sorting Bacterial Genomes Below the Species Rank
• A-G.08: How Private Are DNA Embeddings? Inverting Foundation Model Representations of Genomic Sequences
• A-G.09: TabPFN-Wide: Continued Pre-Training for Extreme Feature Counts
• A-G.10: Short-Context gLM Training for Long-Range Variant Effect Prediction
• A-G.11: NCRP: enhancing long-read classification through neighborhood-consistency refinement and propagation in the overlap graph
• A-G.13: Compounding of rare copy-number variants and polygenic risk: A genetic signature of assortative mating
• A-G.14: ReVSeq Visuals: A Rapid, Modular Environment for Viral Sequence Visualization
• A-G.15: Genome wide eccDNA hotspot propensity estimation from confounder aware features
• A-G.16: SPA-C: an hybrid tool to accurately scaffold genomes using Hi-C and Deep-Learning
• A-G.17: Kente: A Graph-based Pangenomic Approach for Horizontal Gene Transfer Detection in Microbiomes.
• A-G.18: A context-aware framework leveraging LLM-estimated variant effects for individualized variant-to-phenotype interpretation
• A-G.19: Read-Level Classification of HPV Viral Metagenomic Data Using Transformer Architectures
• A-G.20: Characterizing the temporal dynamics of Cancer Hallmarks and somatic mutations across 80,000+ tumors
• A-G.21: Interpreting Structural Variations in Breast Cancer Cell Lines Using 3D Genome Data
• A-G.22: GENA-Web - GENomic Annotations Web Inference using DNA language models
• A-G.23: Mapping and improving the limited sensitivity of SigProfilerAssignment to flat mutational signatures
• A-G.24: Biopsy-Aware Phylogenetic Reconstruction of Cancer Evolution Using Single-Cell Copy Number Profiles
• A-G.25: Evaluating local assembly strategies for long-read structural variant calling
• A-G.26: Multilateration-Based Indexing and Navigation for Error-Tolerant Read Mapping
• A-G.27: Pathoplexus: Building a new kind of pathogen database
• A-G.28: A Fluorescent Probe Under the Microscope Showing Dual Recognition of B-DNA and G-Quadruplex DNA
• A-G.29: SVlog: Unlocking Structural Variation in Rare Diseases through an Extensible Logic Programming Framework
• A-G.30: Predicting gene expression profiles and drug targets from DNA methylation in gliomas
• A-G.31: Unbiased deconvolution of Oxford Nanopore Technologies long reads to reconstruct methylomes at cell type level
• A-G.32: SIEVE: Sparse Interpretable Exome Variant Explainer
• A-G.33: Impact of AI-assisted workflow standardization in a bioinformatics core facility
• A-G.34: NANO-SCOPE: Read-level tumor detection from cfDNA methylomes by Nanopore sequencing
• A-G.35: Modeling Longitudinal Cancer Dynamics from Omics Data: a Systematic Review of Current Limitations and the Road Toward Temporal Generation
• A-G.36: TOGA2 delivers scalable and precise gene annotation and ortholog identification across vertebrate genomes
• A-G.37: GAP-MS: Automated validation of gene predictions using integrated mass spectrometry evidence
• A-G.38: Embedding-based statistical framework enables quantitative gene function representation and hypothesis testing with large language models
• A-G.39: Unmasking the Trypanosoma cruzi Genome: A Comprehensive Map of the MASP Superfamily.
• A-G.40: NeoEvo-AIS: Rethinking Tumor Immunogenicity at the Tumor Level
• A-G.41: TFClassPredict: A Deep Learning Framework for Transcription Factor Binding Site Analysis Using Evolutionarily Conserved DNA-Binding Domain Annotations
• A-G.42: End-to-end deep learning methods for genetic risk prediction of Schizophrenia
• A-G.43: Contrastive Learning of Multiple Sequence Alignments for Phylogenetic Inference
• A-G.44: PanelClone: signature-aware subclonal reconstruction that scales with mutation count
• A-G.45: Large cohorts analysis to identify pathogenic digenic interactions in autoinflammatory disorders
• A-G.46: PlanMine: An Interactive Database for Exploring Planarian Genomics and Regeneration
• A-G.47: Genome Enhancer reveals an IGF-centered regulatory program underlying shared epigenetic mechanisms in Kabuki syndrome subtypes
• A-G.48: Improved reconstruction of transcripts and coding sequences from RNA-seq data
• A-G.49: Chromatin-informed sparsification of neural networks improves gene expression prediction and regulatory interaction recovery
• A-G.50: Ancestry calibrated polygenic risk score for breast cancer risk stratification in an admixed Colombian cohort
• A-G.51: Targeted long-read sequencing for high-resolution repeat profiling in myotonic dystrophy type 1
• A-G.52: Pattern-based segmentation of the methylome reveals chemotherapy-associated changes in ovarian cancer
• A-G.53: Evaluating portability of polygenic scores across ancestries with precision-weighted meta-analysis reveals trait-specific rather than universal poor portability
• A-G.54: Microsynteny and functional relatedness: insights into eukaryotic genome evolution
• A-G.C.01: DifFracTion: Systematic normalization and differential interaction identification for cross-sample comparison of Hi-C datasets
• A-G.C.03: ROTS 2.0: A reproducibility-driven framework for complex statistical models in high-throughput omics
• A-G.C.04: Emergent Operon Structure in Genomic Language Models
• A-G.C.05: From fragmented MAGs to complete chromosomes: refining functional diversity in uncultured Patescibacteria
• A-G.C.06: Ocrelizumab causes transient changes in the composition of intestinal bacteria and modulates the immune response depending on the response to treatment
• A-G.C.07: Efficient Population Structure-Adjusted Logistic Regression for Genome-wide Association Interaction Studies via Clustered Covariates
• A-G.C.08: The SIB RDF knowledge graph network: FAIR in practice
• A-G.C.09: Detecting Antibiotic Resistance in Drug-Free Conditions: An AI-Powered Morphological Approach in Klebsiella pneumoniae
• A-G.C.10: SwissLipids2.0: Towards a Semantically Integrated Lipid Knowledge Resource
• A-G.C.11: Exploring Metabolite-Based Cluster Patterns Associated with Severity in Inflammatory Bowel Disease
• A-G.C.12: Sex-Specific Analyses Reveal Differences in Genetic Architecture and Cardiometabolic Associations of the Retinal Microvasculature
• A-G.C.13: From rosettes to cells: multi-scale shape plasticity in Arabidopsis thaliana under different temperatures
• A-G.C.14: Automatic differentiation enables efficient 13C isotopically non-stationary metabolic flux analysis
• A-G.C.15: How Phosphorylation Affects Peptide Interaction with Adaptor Domains
• A-G.C.16: Navigating Career Pathways with the EMBL-EBI Competency Hub
• A-G.C.17: AmpliPhy improves gene trees by adding homologous sequences without affecting alignments
• A-G.C.18: Towards FAIR and Privacy-Preserving Synthetic Health Data: A Reproducible Pipeline for Generation and Evaluation
• A-G.C.19: Conformal Inference for Gene-Specific Cell Subsets in Single-Cell Differential Expression
• A-G.C.20: A haplotype reference panel constructed from 490,085 UK Biobank genomes improves genotype imputation for global populations
• A-G.C.21: Building Regional AI Capacity in Biosciences: The BiotrAIn Training Model for Latin America
• A-G.C.22: Graph-based Machine Learning approaches for predicting microbiome composition
• A-G.C.23: CNETML2: A Multi-scale Model for Copy Number Alteration Evolution in Cancer
• A-G.C.24: Metadata Accelerator: Improving scientific data descriptions with Natural Language Processing methods (NLP) and Feedback
• A-G.C.25: Discovering Novel Ciliary Genes Through Machine Learning
• A-G.C.26: COMPAS: COntrastive Multimodal Polypharmacology-Aware multi-target drug-target interaction Screener
• A-G.C.27: Improving comparative genomics with orthology uncertainty metrics
• A-G.C.28: FLKit: A Community-Driven Onboarding Toolkit for Federated Analytics and Learning in Health and Life Sciences
• A-G.C.29: HOROSCOPE: Decoding human centromere architecture from short reads using k-mer signatures
• A-P.01: PLM-eXplain: Divide and Conquer the Protein Embedding Space
• A-P.02: A self-assembled protein β-helix as a self-contained biofunctional motif
• A-P.03: Structure-Guided RNA Design via Multinomial Diffusion
• A-P.04: Protein language models for beta-lactamase detection and annotation
• A-P.05: TRPM8 Virtual Screening: The Essential Role of Target-Specific, Inactive-Enriched Machine-Learning Scoring Functions
• A-P.06: A pretrained model for RNA inverse folding with formal grammar representations
• A-P.07: Energy-Matched Diffusion for Antibody Generation
• A-P.08: Gated Modulation Protein Language Model for Prediction of Antimicrobial Peptide Activity
• A-P.09: Integrating Graph Encoders and Protein Language Models for Antibody-Antigen Binding Free Energy Prediction
• A-P.10: CAPRINI-M: An AI-curated Cardiac-Specific Atlas of Protein Interactions in Mice
• A-P.11: A comparison of machine learning strategies for classification and clustering of transmembrane protein-protein interactions
• A-P.12: Multi-Modal Protein Representation Learning with CLASP
• A-P.13: Structure-based virtual screening for TRPM8 modulators
• A-P.14: Beyond Lookup? Quantifying What LLMs Actually Contribute to Enzyme Function Prediction
• A-P.15: Bridging Scales: A Multi-Level Graph Neural Network for Protein Function Prediction
• A-P.16: Ensembling Structure Model Outputs for Nearly Free Performance Gains in TCR-pMHC Interaction Prediction
• A-P.17: A Unified Computational Framework for the Integration of AI Models in Structure-Based Drug Design
• A-P.18: BepiCon: A Geometric Deep Learning Framework for Conformational B Cell Epitope Prediction
• A-P.19: Leakage-controlled evaluation of single-to-multi mutation generalization in fungal azole resistance prediction
• A-P.20: PUMA: Discovery of Protein Units via Mutation-Aware Merging
• A-P.21: SpeciefAI: Multi-species mRNA-level Antibody Framework Generation using Transformers
• A-P.22: WACA-DTA: Water-Aware Geometric Biases for Structure-Conditioned Drug-Target Affinity Prediction
• A-P.23: Swiss-PO. Advancing Cancer Mutation and Structural analysis for Precision Oncology with the Latest Release
• A-P.24: Swiss-PO. Advancing Cancer Mutation and Structural analysis for Precision Oncology with the Latest Release
• A-P.25: FusionPath: Gene fusion pathogenicity prediction using protein structural data and contextual protein embeddings
• A-P.26: Elucidating the Structural Dynamics and Binding Mechanism of ProQ-raiZ Using an ML based Deep-TDA Approach for Enhanced Sampling Simulations: Implications for Novel Antimicrobial Targeting
• A-P.27: Assessing Long-distance Epistatic Interactions in Enzymes
• A-P.28: Computational Design of Anti-VEGF Binders: From Scaffold Optimization to De Novo Design
• A-P.29: CCD2MD: A suite of Packages for Preparing Co-Folded Outputs for Molecular Dynamics Simulations
• A-P.30: Computational Mutational Scanning of DHFR for Mutation-Dependent Inhibitor Preference
• A-P.31: A Scalable Integer Programming Model for RNA Secondary Structure Prediction
• A-P.32: Novel universal domain-centric method for protein classification
• A-P.33: BrightDB : Brightness Protein Database with fast accession to millions of proteins and an interactive dashboard
• A-P.34: Reference-Free Ranking Method for RNA 3D Models
• A-P.35: TmProt 1.0: ML-based Tool for Protein Melting Temperature Prediction with Cross-Method Validation on Biophysical Data
• A-P.36: Accelerating Multiple Myeloma Diagnosis with Proteomics & Machine Learning
• A-P.37: Alpha&ESMhFolds: An updated database for the comparison and functional annotation of predicted structural models for the human proteome
• A-P.38: Exploring the Proteomics Dark Matter: Non-Canonical Peptide Identification and the Quantification of all Precursor Ions
• A-P.39: Characterization of SLC25A38 as a Mitochondrial Pyridoxal 5'-Phosphate Transporter
• A-P.40: Uncovering the Dark Side of the Immunopeptidome
• A-P.41: Stability-informed low-N learning of protein fitness landscape
• A-P.42: Exploring Proteomic Markers in Local and Systemic Fluids to Advance Understanding of NIHL
• A-P.43: Unveiling the Diversity of Catalytically Inactive Long-B Prokaryotic Argonaute Proteins
• A-P.44: Characterization of a highly diverged Cas7 homolog in Felix phages
• A-P.45: Identification of new P-loop NTPase-like families
• A-P.46: Machine learning approaches for charting the functional and structural landscape of protein ubiquitination
• A-P.47: Multi-property Evaluation of Nanobodies Designed by an AI-Driven Pipeline for Binding to Specified Sites with Experimental Validation
• A-P.48: SELECTIVE MODULATION OF CHEMOKINE-LIKE RECEPTOR 2 BY SMALL MOLECULES
• A-P.49: Multiscale computational dissection of CCRL2-chemerin-CMKLR1 axis
• A-P.50: Biological meaning in protein embedding space is resolution-dependent
• A-P.51: DeepAlloWeb: A Web Server for Allosteric Pocket Prediction and Attention Based Interpretation
• A-P.52: A Machine Learning Framework for High-Performance Prediction of Enzyme-Substrate Interactions Using Advanced Protein Embeddings and Molecular Fingerprints
• A-S.B.01: Integrative Machine Learning of CSF and Plasma Multi-Omics Identifies Progression-Relevant Biomarkers in Parkinson's Disease
• A-S.B.02: ESM-ECForest: A Two-Stage ESM-Based Machine Learning Pipeline for Enzyme Function Prediction
• A-S.B.03: {llumen}: An Agentic R Framework for Secure and Scalable Biomedical Analyses with Large Language Models
• A-S.B.04: A Deep Learning Framework for Inferring Protein-Protein Interaction Differences Across Cellular and Tissue Contexts
• A-S.B.05: Genomic Feature Transformation for Multi-Omics Integration in Clinical Prediction Models
• A-S.B.06: Graph-based Modeling of Microbe–Drug Interaction Networks using MASI
• A-S.B.07: Combining mechanistic modelling with biobank data to assess the effect of pharmacological osteoporosis therapy in women with or without HRT
• A-S.B.08: AIBioAgentTools: An MCP-Based Bioinformatics Tool Repository for LLM-Driven Autonomous Bioinformatics Pipeline Execution
• A-S.B.09: Synthetic Data Evaluation Metrics in Life Sciences: An ELIXIR Scoping Review
• A-S.B.10: Coralysis: a multi-level algorithm for effective integration of imbalanced single-cell data
• A-S.B.11: Physics-Informed Learning-Based Efficient Computation of Pareto Frontiers for Biological Process Understanding
• A-S.B.12: LinearCytoVAE: A conditional variational autoencoder with linear decoder identifies signalling modules in high-dimensional cytometry data
• A-S.B.13: RingNet: an interactive platform for multi-modal data visualization in networks
• A-S.B.14: Probabilistic Modeling of Calcium-Driven ADD3 Isoform Activation Reveals Context-Dependent Metastatic Programs
• A-S.B.16: A Mathematical Model for the Co-regulation of Antiviral and Inflammatory Genes Mediated by NF-κB and IRF-3 post Viral Infections
• A-S.B.17: Sample-specific protein-protein interaction networks inferred from transcriptomics and proteomics show high similarities
• A-S.B.18: InSTaPath: Integrating Spatial Transcriptomics and Histopathology Images via Multimodal Topic Learning
• A-S.B.19: PertAL: an efficient and robust active learning framework for budget-constrained single-cell perturbation screens
• A-S.B.20: DanSyn: Hybrid Structural-Functional Representations for Drug Synergy Prediction
• A-S.B.21: WiNN enables selective correction of run-order drift and batch effects while preserving biological signal in metabolomics data
• A-S.B.22: How to Encode Drugs, Genes and Cells: Benchmarking Encodings for Cancer Cell Viability Prediction
• A-S.B.23: GeneSelectR 2.0: Integrating Stability, Utility, and Biological Relevance for Robust Feature Selection in High Dimensional Biomedical Datasets
• A-S.B.24: Inference of cell-cell communication Boolean networks
• A-S.B.25: Friendship-Like Differential Co-expression Networks: Identifying Tumor-Educated Platelets Driver Genes in Glioma via Structural Imbalance
• A-S.B.26: Backtrack-free network propagation with in-degree normalization
• A-S.B.27: Geometry-Aligned Flow Matching with Soft Tangent-Space Dynamics for Unpaired Single-Cell Perturbation Prediction
• A-S.B.28: Machine learning-based morphometric profiling of patient-derived motor neurons as a scalable platform for amyotrophic lateral sclerosis drug discovery
• A-S.B.29: SegTraQ: a Python package for segmentation quality control in spatial transcriptomics data
• A-S.B.30: Genome-Scale Metabolic Model of Pseudomonas aeruginosa PAO1 with Integrated Quorum Sensing-Related Pathways
• A-S.B.31: Pneumatic Microvalve-Driven Multi-Way Fluorescence-Activated Sorting Platform
• A-S.B.32: GNNMutation: a heterogeneous graph-based framework for cancer detection
• A-S.B.33: Integrative Multi-Omics and Network Analysis Identifies Condition-Specific Virulence Signatures in Carbapenem-Resistant Acinetobacter baumannii
• A-S.B.34: Deep Generative Flow Matching for Unpaired Translation Across Heterogeneous Biological Domains
• A-S.B.35: A structured evaluation and benchmarking of Boolean modelling tools for systems biology
• A-S.B.36: How to predict effective drug combinations – moving beyond synergy scores
• A-S.B.37: Multiomics Integration for Exercise Personalisation: Insights from the ACTIBATE Trial on Brown Adipose Tissue Activation
• A-S.B.38: PGCC Explorer: An Interactive Web Platform for Integrative Analysis of Drug Responses in Polyploid Giant Cancer Cells
• A-S.B.39: Multi-Relational Hypergraph Representation Learning for Predicting High-Order Biomedical Associations
• A-S.B.40: Graph-based integration of microbiome and metabolome data stratifies inflammatory bowel disease, revealing biomarkers associated with impaired tryptophan and bile acid metabolism.
• A-S.B.41: Volatile organic compound dynamics of Lantana camara across an urban disturbance gradient in forest fragments of Delhi, India
• A-S.B.42: MANTIS: Nonlinear Modeling of Tensor-Structured Multi-Omics Data
• A-S.B.43: Linking global insertion sequence diversity to ecosystem gradients using neural networks and mixture modeling
• A-S.B.44: LOINC Laboratory Diversifier: A Benchmark Dataset for Interoperability
• A-S.B.45: Gene co-expression network analysis reveals shared molecular links between cardiometabolic traits and fatty liver disease
• A-S.B.46: Multimodal cheminformatic and network biology analysis enables deeper mechanistic understanding of PFAS-induced hepatotoxicity
• A-S.B.47: MEDORA: Biologically Guided Inference of Minimal Culture Media at Genome Scale
• A-S.B.48: High-Content Morphological Profiling Improves Drug Synergy Prediction
• A-S.B.49: Modeling longitudinal disease dynamics in Systemic Lupus Erythematosus using representation learning
• A-S.B.50: The Power of Regulation: Benchmarking Graph-Flow vs Shortest-Path Causal Algorithms for Target Discovery and Validation
• A-S.B.51: Atlantis: An integrative database for human proteome structural and functional sites
• A-S.B.52: Assessing and Validating Flux Predictions in CHO Genome-Scale Metabolic Models
• A-S.B.53: AI-Immunologyâ„¢ designs shared ERV-derived therapeutic vaccine for acute myeloid leukemia
• A-S.B.54: An interpretable deep-learning framework (BRS) identifies synergistic root-exudate metabolite combinations associated with nitrification inhibition in wheat
• A-S.B.55: Multi-omics integration framework for incident disease risk prediction in cohort studies.
• A-S.B.56: Discrimination of malignant cells from normal cells in the tumor microenvironment using single-cell transcriptomics data
• A-S.B.57: High-Resolution Spatial Transcriptomics Unravels ICI Dependent Immune Escape in Endometrial Cancer
• A-S.B.58: RetFit: A Novel Deep Learning Biomarker based on Cardiorespiratory Fitness derived from the Retina
• A-S.B.59: Deep generative multi-omics mosaic integration from cancer cell lines, organoids, and tumors
• A-S.B.60: The Perturbation Catalogue: An Integrated Resource for Exploring the Impact of Genetic Perturbations
• A-S.B.62: Directional Stochastic Drift Enables Predictive and Interpretable Omics Modeling
• A-S.B.63: An Integrated Multi-Omics Platform for Interactive Visualization and Functional Discovery of Plant Regulatory Elements
• A-S.B.64: Unlocking the Diagnostic Potential of Proteomics and Machine Learning in the Clinical Laboratory
• A-S.B.65: Vessel spatial analysis (VeSpA): an automated pipeline for vessel segmentation and morphometric quantification in whole slide images (WSI) with integrated QuPath extension
• A-S.B.66: Unlocking the potential of PubMed Central supplementary data files
• A-S.B.67: UK DRI Dementia Data Nexus - Data without Borders
• A-S.B.68: Multi-modal patient similarity networks for resilience modelling in hypertension
• A-S.B.69: DataMap: Enabling Data-Driven Discovery through a Curated Biological Knowledge Graph
• A-S.B.70: From Vascular Geometry to Function: Haemodynamic Modelling of Retinal Blood Flow
• A-S.B.71: The quiet passenger - modelling metabolic interactions between Wolbachia, a common reproductive parasite of insects, and its host Drosophila melanogaster.
• A-S.B.72: CrossPoE: Survival-Calibrated Cross-Modal Translation for Multi-Omics Under Block-Wise Missingness
• A-S.B.73: scSurv: a deep generative model for single-cell survival analysis
• A-S.B.74: DrugMir: An Explainable Multi-Omics Framework for Drug Response Prediction with microRNA Signatures in Cancer
• A-S.B.75: Sex-Specific Gut Microbiota in Major Depressive Disorder
• A-S.B.76: Effects of Checkpoint Inhibitor Therapy on Immune Cell Composition in Primary Tumor Sites and Metastases
• A-S.B.77: ChEBI-N - An Updated Ontology-Based Chemical Enrichment Analysis Tool
• A-T.01: Raw signal segmentation for estimating RNA modification from Nanopore direct RNA sequencing data
• A-T.02: spacedeconv: deconvolution of tissue architecture from spatial transcriptomics
• A-T.03: omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data
• A-T.04: scCont: Interpretable Contrastive Learning for Functional Characterization of Cellular State Transitions
• A-T.05: EmpiReS: Differential Gene Expression and Differential Alternative Splicing
• A-T.06: Beyond Coding Potential: Using Multimodal learning to probe the lncRNA/mRNA boundary
• A-T.07: scTransformer: integrating gene regulatory priors into Transformer attention for interpretable scRNA-seq
• A-T.08: PanXpress: Gene expression quantification with a pan-transcriptomic gapped k-mer index
• A-T.09: Deep Generative modeling Reveals Multi-Phase Regenerative Cell Dynamics in Arabidopsis Roots
• A-T.10: Representing transcription factor dimer binding sites using Forked-Position Weight Matrices and Forked-Sequence Logos
• A-T.11: Integrative Transcriptomic and Machine-Learning Analysis Reveals APC-Associated Chemotherapeutic Response Programs in Triple-Negative Breast Cancer
• A-T.12: ChiMER: Integrating chromatin architecture into splicing graphs for chimeric enhancer RNAs detection
• A-T.13: A Systematic Evaluation of Single-Cell Batch Integration Metrics and sBEE: A Robust New Metric
• A-T.14: PATH: Spatial Inference of Pathway Activation from H&E Images
• A-T.15: Differential network centrality analysis identifies signatures in tumor-educated platelets
• A-T.17: MIL2Het: Learning Patient Phenotypes from Single-Cell Heterogeneity with Multi-view Prior Knowledge-informed Graph Learning
• A-T.18: MPRA-MNIST: A Starter Kit for Deep Learning of Gene Regulatory Regions from Massively Parallel Reporter Assays
• A-T.19: Spatially Resolving Tumor Heterogeneity in HNSCC: Targeting Tumor Microenvironment Markers
• A-T.20: On the prediction of observed cell states from inferred gene regulatory networks
• A-T.21: Prediction of Split Open Reading Frames in cardiovascular cell types
• A-T.22: Decoding embryonic neurogenesis of Octopus vulgaris using trajectory analyses
• A-T.23: Survey od promoter upstream elements across metazoans
• A-T.24: Multi-omics profiling of defined Ca2+ signals in mast cells
• A-T.25: Deep Learning-Informed Interpretation of 3'UTR Variants Controlling Inflammation
• A-T.26: Beyond gene expression statistics: interpretable gene contributions from sequence-based foundation models in single-cell data
• A-T.27: A detailed atlas of transcribed regulatory element activity, gene expression, and protein abundance in diverse human skeletal muscles
• A-T.28: Joint inference of guide assignment and perturbation effects in heterogeneous single-cell CRISPR screens
• A-T.29: A single-cell atlas linking intratumoral states to therapeutic vulnerabilities across cancers
• A-T.30: QUASAR: Integrating domain adaptation and bootstrapping for accurate bulk RNA‑seq cell-type Deconvolution in infected organs
• A-T.31: Decoding cis-regulatory logic in early skeletal muscle development with interpretable deep learning
• A-T.32: Gene regulatory network-based dynamic modeling of cellular differentiation trajectories
• A-T.33: DRAFT: Dynamic Gene Regulatory Network Inference with Graph-Recurrent Attention from Multi-omics Single-cell Trajectories
• A-T.34: Sex-by-disease interaction modelling uncovers divergent molecular responses in ankylosing spondylitis
• A-T.35: Analysis of Pipeline Robustness and Primer Binding Patterns in Split-Pool scRNA-seq
• A-T.36: Epistatic gene interactions in the evolution of robots
• A-T.37: Cell-type-specific evolutionary dynamics of sex-biased gene expression across mammalian organs
• A-T.38: Beyond the Paradigm: When Foundations Aren't Enough for Spatial and Single-Cell Omics
• A-T.39: SCENE: A Framework for Single-cell Gene Co-expression Network Analysis Across Cell Types and Conditions
• A-T.40: Unbiased detection of non-canonical back-splicing events reveals hidden viral circular RNAs
• A-T.41: SPLISOFORMS: Exploring the Structural and Functional Impact of Alternative Splicing in Cancer
• A-T.42: Cell type composition drives patient stratification in single-cell RNA-seq cohorts
• A-T.43: Integrative Transcriptomic Profiling of iPSC-Derived Podocyte Injury Reveals Shared and Disease-Specific Molecular Signatures
• A-T.44: Benchmarking Regulatory Variant Effect Predictors across Three Distinct MPRA Cellular Contexts
• A-T.45: Transcriptomic Profiling Reveals Chamber-Specific Remodeling in Feline Hypertrophic Cardiomyopathy
• A-T.46: Spatial Cell-Cell Communication in Alzheimer's Disease
• A-T.47: Imbalance-aware differential expression reveals ancestry-specific cancer pathways
• A-T.48: Modeling cis-regulatory variation in human brain enhancers across a large Parkinson's Disease cohort
• A-T.49: Benchmarking Cell-Level, Pseudobulk, and Metacell Differential Gene Expression Analyses for Single Nucleus Transcriptomics Data – A Real-World Use Case
• A-T.50: Igniting full-length isoform analysis in single-cell and spatial RNA-seq data with FLAMESv2
• A-T.51: Histone Deacetylases 1 and 2 Regulate Intestinal T Cell Subsets and are Essential for Th1/Th17 Immunity against Citrobacter rodentium
• A-T.52: Democratizing hierarchical cell type deconvolution with HIDE-deconv
• A-T.53: MetaTIS: A tool to predict all types of translation initiation sites in human
• A-T.54: Computational analysis of single-cell and bulk transcriptomic data reveals T cell-intrinsic and -extrinsic determinants of TIL therapy feasibility in glioblastoma
• B-B.01: Expanding Genomic Resources for Heritage Science: Characterization of Selected Microbial Isolates from Salt-Weathered Historic Sites
• B-B.02: Metagenome-guided discovery of novel laccases for biotransformation by geographically-diverse human gut microbiota
• B-B.03: Connecting microbial species distributions with functional potential across global ecosystems using mOTUs
• B-B.04: zDB: bacterial comparative genomics made easy
• B-B.05: Data-driven prediction of chemical impacts on wild mouse populations through integration of lab-based mouse data
• B-B.06: Advancing FAIR biodiversity genomics with open-source solutions: The Swedish Reference Genome Portal and DivBase
• B-B.08: Tracking Respiratory Syncytial Virus Dynamics in Wastewater During the 2024-2025 Season in Switzerland
• B-B.09: Genome context interpretation of dark protein functions with metaGCsnap
• B-B.10: An automated metagenomic pipeline reveals wastewater driven spatial and seasonal structuring of river viromes
• B-B.11: Conformal prediction enables uncertainty quantification with formal statistical guarantees for machine learning classification of Salmonella Typhimurium animal host
• B-B.12: Strain-level meta-analysis of metagenomics data in haematological cancer
• B-B.13: GLADE: Accurate inference of Gains, Losses, Ancestral genomes, and Duplication Events for comparative genomics
• B-B.14: Wheat Alliance: identifying plant genes driving recruitment of beneficial microbes
• B-B.15: Comparative Analysis of Metagenomic and Isolate Sequencing: A Case Study Targeting Carbapenemase-Producing Klebsiella pneumoniae in Influent Wastewater in Germany
• B-B.16: Variations in the latitudinal diversity gradients of the ocean microbiome
• B-ELIXIR.01: Crop yield prediction from integrated heterogeneous data sources using machine learning
• B-ELIXIR.02: BioTerm-Bench: A Harbor-Compatible Benchmark for Tracking Open-Model Progress on Terminal Bioinformatics Tasks
• B-G.01: Circulating DNA reveals nucleosome occupancy patterns that are associated with nucleosome-DNA affinity and are affected in cancer
• B-G.02: Genome-wide association analysis of primary response to anti-TNF therapy in inflammatory bowel disease
• B-G.03: Exploring plasmids within human gut microbiomes via Micro-C metagenomics
• B-G.04: Improved tumor-only variant calling and mutation burden estimation with VarNet-T
• B-G.05: Gene2Phenotype (G2P): a database of detailed, structured gene-disease associations
• B-G.06: Longitudinal cfDNA methylation profiling by EM-seq and TAPS+ for early breast cancer biomarker discovery
• B-G.07: Methylation profile analysis of 'Candidatus Phytoplasma mali' infected apple leaves
• B-G.08: NanoCanvas: A Unified Interactive Browser for Multimodal Nanopore Sequencing Data
• B-G.09: SNEEP: SNV exploration and analysis using epigenomics data
• B-G.10: Assessing SNV and SV Callers using HiFi Long Read WGS to Find Causal Variants for Mendelian Traits in Goats
• B-G.11: The nationwide genomic characteristics and phylogenetic evolution of ST23-K1 hypervirulent Klebsiella pneumoniae in relation to virulence and antimicrobial resistance acquisition
• B-G.12: STOAT : a tool for pangenome-guided genome wide association studies
• B-G.13: A reference-free pangenomic pipeline to uncover transposable element dynamics across genomes
• B-G.14: Multi-feature predictive modeling of novel cancer predisposition genes using pan-cancer data
• B-G.15: Genomic scars of chemotherapy mark the emergence of resistance in childhood cancer
• B-G.16: LongcallD: joint calling and phasing of small, structural and mosaic variants from long reads
• B-G.17: Integration of Chromatin Interaction Maps with GWAS Identifies TBKBP1 as a Novel Chronic HBV Susceptibility Gene
• B-G.18: Pangenome graph annotation
• B-G.19: Improved 16S rRNA-focused computational methods for bacterial strain identification from long read datasets
• B-G.20: Ontology-Driven Prioritization of Candidate Genes in Neurodevelopmental Disorders
• B-G.21: Sincei: A toolkit for exploring single-cell epigenomics data
• B-G.22: Methylation-aware read representation for metagenomic classification and host decontamination
• B-G.23: A Synthetic Data Framework for Evaluating Structural Variant Callers in Tumor-Normal Long-Read Sequencing
• B-G.24: polars-bio: fast, scalable, and out-of-core genomic intervals and format I/O for Python DataFrames
• B-G.25: vepyr: a fast, scalable, and composable Apache DataFusion-based variant annotation engine
• B-G.26: MSClust: de novo clustering of single-cell methylome sequencing data
• B-G.27: Benchmarking knowledge graph embedding models for the prediction of oligogenic combinations
• B-G.28: hicVerse: A modular platform for scalable exploration of harmonized Hi-C datasets across human samples
• B-G.29: Introducing the Y-chromosomal ancestral-like reference sequence - Improving the capture of human evolutionary information
• B-G.30: Sparrowhawk: running bacterial bioinformatics analyses anywhere, locally, with WebAssembly
• B-G.31: Adipose core genes drive cardiovascular risk via EMT and adipogenesis pathways
• B-G.32: Determinants of functional burden pleiotropy and gene dosage responses across human traits
• B-G.33: Multi-scale characterization of epigenomic alterations in pediatric brain tumors
• B-G.34: ROS-Driven Somatic Mutation Landscape and Tumorigenesis in Prx1 Knockout Mice
• B-G.35: tfClone: Accurate Determination of Haplotype Specific Clonal Copy Number Profiles in Circulating Tumour DNA
• B-G.36: Advancing copy-number phylogenetics through updates to MEDICC2
• B-G.37: W-ASAP: Rapid Interactive Wastewater-based Viral Variant Detection
• B-G.38: Automatic reanalysis of genetic data from patients with Primary Ciliary Dyskinesia
• B-G.39: Optimizing genetic association tests for small cohorts
• B-G.40: Transposable element diversity and its potential role in symbiosis regulation explored using new chromosome-scale assemblies of Medicago truncatula ecotypes
• B-G.41: DNA damage assessment from panel sequencing data and its use in the study of survivability
• B-G.42: Genomic Instability Pattern Analysis in Advanced Stages of High-Grade Serous Ovarian Cancer
• B-G.43: Phylogenetic tree inference from single-cell RNA sequencing data
• B-G.44: An Integrated Framework for Multi-Omics Long-Read Analysis at Single-Molecule Resolution
• B-G.45: A reproducible workflow for HEK293 methylome profiling during adaptation to suspension growth
• B-G.46: SNooPy: a statistical framework for long-read metagenomic variant calling
• B-G.47: Impact of Sperm Methylome on In Vitro Produced Embryo Methylome, Transcriptome and Development
• B-G.48: Dysregulating genomes - mapping how breaking DNA-lamina interactions drives muscular dystrophy
• B-G.49: A lesson of grammar: unraveling Transcription Factors DNA-binding syntax rules in the model plant Arabidopsis thaliana
• B-G.50: Epigenomic Rewiring of Enterocyte Chromatin During Post natal Salmonella Typhimurium Infection
• B-G.51: Detection of haplotype-specific contacts in phased chromatin contact maps with Genome Architecture Mapping
• B-G.52: Somatic variant detection using a personalized pangenome reference: a study of a paediatric acute myeloid leukaemia patient
• B-G.53: Prioritizing non-coding variants to discover mutations governing agronomic traits in plants
• B-G.54: Plasma cfDNA fragmentomics provides insight into Preeclampsia pathophysiology
• B-G.C.01: Computational Insights into the pH-Dependent Behavior of Ipilimumab-CTLA-4
• B-G.C.02: termal: Interactive Exploration of Multiple Sequence Alignments in the Terminal
• B-G.C.03: A Comparative Study of QSPR Methods on a Unique Multitask PAMPA dataset
• B-G.C.04: Repurposing blood whole-genome sequencing to study leaky gut
• B-G.C.05: Classification of Metabolic Reaction Graphs using Graph Transformers
• B-G.C.06: A Tissue-Independent Landscape of Macrophage Dynamics in Regeneration
• B-G.C.07: Semantic annotation for artifact detection in spatial proteomics using DINOv3 as a vision foundation model
• B-G.C.08: BGC2NP-CLIP: Contrastive Cross-Modal Retrieval Between Biosynthetic Gene Clusters and Natural Products
• B-G.C.09: DEEPScreen++: A Modular Image-Based Deep Learning Framework for Drug–Target Interaction Prediction
• B-G.C.10: Phylogenomic surveillance of outbreaks and antimicrobial resistance across German hospitals
• B-G.C.11: From Sequence to Significance: The LCRPlatform Webserver for Similarity-Driven Functional Annotation Transfer in Low-Complexity Protein Regions
• B-G.C.12: Segmenting Organoids: Evaluating Classical, Specialised, and Generalist AI Approaches for Brightfield Microscopy
• B-G.C.13: Interpretable Single-Cell Transcriptomics Analysis using Generalized Additive Variational Autoencoders
• B-G.C.14: Determinants of Missense Pathogenicity in Usherin: Evaluating Sequence and Predicted-Structure Features
• B-G.C.15: An extension of over-representation methods based on metabolic distance: application to the interpretation of metabolic perturbation in relation to key events
• B-G.C.16: The IDERHA Platform: Federated data governanace and analysis for cross-institutional health research
• B-G.C.17: Receptor signaling architecture links pharmacology to subjective psychedelic experience across chemical classes
• B-G.C.18: GExMix: Enhancing Molecular Representation with Stratified Data Augmentation for Drug Response Prediction
• B-G.C.19: Evaluating the reliability of drug response prediction across transcriptomic domains
• B-G.C.20: A Reproducible R Workflow for Flow Cytometry Data Analysis
• B-G.C.21: LitSABER-chem agent: LLM-driven extraction and prioritization of Enzymatic Reactions from Scientific Literature for Rhea
• B-G.C.22: Content-Based Retrieval for High-Dimensional Biological Data with Multi-Resolution Hierarchical Similarity
• B-G.C.23: TRUSTA: Taxon-Aware Error Thresholding for Reliable Species-Level Assignment from Full-Length 16S rRNA Sequencing
• B-G.C.24: Multimodal Machine Learning for Lung Function Prediction in Interstitial Lung Disease
• B-G.C.25: FENNEC: Fine-Tuned Ensemble Neural Networks Accelerate Chemically Modified siRNA Design and Screening
• B-G.C.26: Learning latent symmetries from related bioassays for data-efficient toxicology
• B-G.C.27: Modelling gradual and saltational evolutionary dynamics of chromosomal instability across cancers
• B-G.C.28: MariNET: A Linear Mixed Model Framework for Network Analysis of Longitudinal EHR Data
• B-G.C.28: Shifting the spotlight to unexpected guests: A workflow for the taxonomic and functional profiling of non-target microorganisms in transcriptomics datasets
• B-P.01: STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
• B-P.02: Promiscuous Mitochondrial Targeting Under Proteotoxic Stress Rewires Cytosolic Proteostasis
• B-P.03: Nucleic acid 3D structure search and alignment with GTalign
• B-P.04: PUCAR : Protein unit discovery using community detection on pLM attention-weighted residue graphs
• B-P.05: CNN-3Dswappred: Enhanced Sequence-Based Prediction of Protein Domain Swapping
• B-P.06: Evaluating Confidence Scores in OpenFold3 for Protein Interaction Prediction
• B-P.07: Scaling and democratising structure-based protein function prediction with metagenomic-deepFRI
• B-P.08: Phyre2.2: A web server to predict protein structure and protein/ligand complexes
• B-P.09: UniProt Pan Proteomes: a scalable resource for comparative proteome analyses and exploring species diversity
• B-P.10: Probing multi-omic datasets to unravel multi-stability mechanisms in CAZymes from extreme biomass-rich environments.
• B-P.11: Computational analysis of protein-protein interactions in biocondensates
• B-P.12: AbMuSiC: a physics-based method for predicting the change in binding affinity upon mutation in antibody-antigen complexes
• B-P.13: Identifying Candidate Precursors of Cyclic Repetitive Antibody Targets with the Peptidic Antigen Sequence Complexity Analyzer (PASCA)
• B-P.14: InterProScan 6: a modern large-scale protein function annotation pipeline
• B-P.15: Integrated Computational and Experimental Characterization of a Novel Metagenome-Derived Tryptophan Indole-Lyase
• B-P.16: In Silico Reverse Vaccinology Approach for Multi-Epitope Vaccine Design Against Stenotrophomonas maltophilia
• B-P.17: Benchmarking HERMES for TCR-pMHC Binding Prediction Across Diverse Systems Using AlphaFold3-Predicted Structures
• B-P.18: Rhea, a FAIR resource of expert curated biochemical and transport reactions
• B-P.19: Structure-guided embeddings predict CYP substrate specificity: engineering citrus flavors for climate resilience
• B-P.20: Target-Specific De Novo Design of Drug Candidate Molecules with Graph-Transformer-Based Generative Adversarial Networks
• B-P.21: Joint-Embedding Predictive Representation Learning for Protein-Ligand Pose Generation
• B-P.22: Co-folding based sites-of-metabolism prediction of cytochrome P450 2C9
• B-P.23: ISS-Priority: Priority-guided graph propagation for indirect protein structure retrieval
• B-P.24: Mutation-Specific ∆∆G Modeling for Alanine Substitutions Improves Stability Prediction and Reveals Distinct Constraint Regimes
• B-P.25: Decoding protein functional determinants: A simple adapter-based module with interpretable attention
• B-P.26: Target-Conditioned Molecule Generation using Protein and Chemical Language Models with Bioactivity-Guided Post-Training
• B-P.27: Apo, Holo, and Everything in Between: What State Are AlphaFold Binding Pockets In?
• B-P.28: Structure-Function Analysis of Arabidopsis Cellulose Synthases and Small Molecule Inhibitors Using Computational and In Vivo Approaches
• B-P.29: Integrated computational and experimental analysis of a matricellular protein-peptide interaction enables rational antifibrotic binder design
• B-P.30: Coordinated pMHC Dynamics Reveal TCR-Recognizable States for Neoepitope Prioritization in Personalized Cancer Vaccine Design
• B-P.31: ZFP-CanPred: Predicting the Effect of Mutations in Zinc-Finger Proteins in Cancers Using Protein Language Models
• B-P.32: Functional aggregation-prone regions mediate carbohydrate-binding and harbour disease-associated mutations
• B-P.33: Construction and Evaluation of a Multimodal Fusion-Based Model for Compound Protein Interaction Prediction
• B-P.34: Order–disorder interfaces in viral pRb inactivation: molecular dynamics insights into SLiM-mediated recognition and implications for interaction databases
• B-P.35: Statistical modelling of TCR sequences provides robust quality control for repertoire datasets
• B-P.36: Uncovering Hidden Proteomic Landscapes: Isoform-Resolved Drug Response Analysis with NOMAD
• B-P.37: Whole-Exome Sequencing and Structural Modeling Identify Candidate Drivers in a Multiplex Multiple Sclerosis Family
• B-P.38: autoeval: A robust toolkit for standardized ad-hoc protein language model evaluation
• B-P.39: Interpreting Structural Representations and Attention in Pairformer-Based Protein Folding Models for Protein–Protein Interactions
• B-P.40: Structure based scoring of AI-predicted peptide-GPCR complexes for receptor annotation in a non-model insect genome
• B-P.41: AMP-DiT: Antimicrobial Peptide Design with AMP-classifier Conditional Diffusion Transformers
• B-P.42: AMP-BindGen: Guided Protein Design for Antimicrobial Peptides and Targeted Inhibitors Against AMR
• B-P.43: Missense 3D portal, enabling investigation and interpretation of missense variants in their structural context
• B-P.44: Designing Novel Enzymes with a Transformer-Guided Generative AI Framework
• B-P.45: Benchmarking Structural Alignment Tools for Protein-Protein Interfaces in Template-Based Docking
• B-P.46: The Role of Local Energetic Frustration Across Conformational Ensembles of Disordered Proteins
• B-P.47: A Replicate-Based Molecular Dynamics Framework for Mapping Variant-Induced Structural and Network Perturbations in Drug-Metabolising NAT2 Enzyme
• B-P.48: Benchmarking Deconvolution Tools for Proteomics Data
• B-P.49: Why Think Multimodal? Data Treatment for Integrated Structural Biology
• B-P.50: Reading TEA-Leaves for de novo protein design
• B-P.51: Identification of Highly Conserved Conformational Epitopes on the SARS-CoV-2 Spike Glycoprotein: A Computational Foundation for Broad-spectrum Antibody Design
• B-P.52: BiochemicalAlgorithms.jl for Stochastic Protein Energy Minimization
• B-S.B.01: Interpretable Gene Program analysis for single-cell transcriptomics: A comparative Methodological framework with application to Idiopathic Pulmonary Fibrosis
• B-S.B.02: DERIVING THREE ONE DIMENSIONAL NMR SPECTRA FROM A SINGLE EXPERIMENT
• B-S.B.03: Standardized benchtop NMR workflows for metabolomic profiling across multiple human biofluids
• B-S.B.04: Target-Aware Molecular Representations: Biologically Supervised Contrastive Pretraining (BioSCL) of Graph Neural Networks
• B-S.B.05: LLM Driven Knowledge Graph Construction for Human Organoid Omics Datasets
• B-S.B.06: Multi-agent approach for the synchronization of circadian clocks
• B-S.B.07: A Dynamic Bioprocess Modeling Framework for Pseudomonas putida Integrating Metabolic and pH Effects
• B-S.B.08: Functional reconstruction of drug response dynamics from perturbation single-cell RNA-seq
• B-S.B.09: Reconstructing and analyzing patient-specific gene regulatory networks using the SiSaNA command line interface
• B-S.B.10: The function of national data portals for infectious disease research
• B-S.B.11: Comprehensive Bioinformatic Analysis of Cisplatin-Induced Processes in Tumor Cells
• B-S.B.12: ANIMA: predicting protein-protein interactions across species
• B-S.B.12: One-hot news: drug synergy models shortcut molecular features
• B-S.B.13: Model-guided design of microbial communities for novel fermented foods with optimized flavor profiles
• B-S.B.14: Data-driven identification of possible transmission sites for carbapenem-resistant Gram-negative bacteria
• B-S.B.15: Assessing the reliability of viral host prediction models: A comparison of temporal and random data partitioning
• B-S.B.16: A Geometric View of Community Metabolism
• B-S.B.17: A robust ecosystem for omics data enabling dementia research
• B-S.B.18: Large-scale single-sample regulatory network inference across 9778 tumors uncovers clinically relevant heterogeneity
• B-S.B.19: DocBot: An LLM-powered natural language interface to pan-EMBL documentation
• B-S.B.20: Ground Truth-Based Validation and Benchmarking of Interpretable Neural Networks for Biological Inference
• B-S.B.21: Interpretable AI for Risk Assessment (IARA): A Graph Neural Network Approach for Organ-Specific Hazard Prediction Using a Biomedical Knowledge Graph
• B-S.B.22: Learning Features and Paths of the Vaccine-Induced Immune Response to Tuberculosis via Partial Correlation
• B-S.B.23: A latent generative model for RNA-informed gRNA assignment and uncertainty quantification in single-cell CRISPR screens
• B-S.B.24: From Genes to Cell Types: A Network-Based Investigation of ASD-ADHD Comorbidity
• B-S.B.25: Multi-omics data analysis unveils treatment opportunities in Brain Metastases
• B-S.B.26: Extracellular matrix-driven patient stratification and network modeling reveal distinct molecular grades with potential clinical implications
• B-S.B.27: Towards Stable Clustering in Hierarchical Variational Autoencoder Models for Multi-Omics Cancer Data Integration
• B-S.B.28: Integrated TFs-focused CRISPRa Screening and Multi-omic Profiling Identify PPARD as a Master Scaffold Orchestrating Hybrid Phenotypes in TNBC
• B-S.B.29: MolMeDB RDF - integration of membrane transport information to a wider biological data ecosystem
• B-S.B.30: A knowledge graph framework for multimodal genotype-phenotype integration in rare genetic diseases
• B-S.B.31: MetaDiffusion: A process-aware conditional diffusion model for multi-species spatial range reconstruction from sparse observations
• B-S.B.32: KGATE: A modular autoencoder for graph representation learning applied to Biomedical Knowledge Graphs
• B-S.B.33: AgentIMC: An Interactive Workflow for Imaging Mass Cytometry Analysis of the Sarcoma Microenvironment
• B-S.B.34: From Black Box to Biology: Explainable Multimodal Cancer Survival Prediction
• B-S.B.35: Neuroblastoma single cell heterogeneity emerges as a combination of embryonic and cancer programs shaped by copy number alterations
• B-S.B.36: A Mixed-Effects Framework for Small-N Intra-Host Viral Evolution Reveals Tissue-Specific Constraints in an Understudied Arbovirus
• B-S.B.37: Tau hyperphosphorylation disrupts microtubule organization: insights from multi-omics and super-resolution imaging of a neuronal tauopathy model
• B-S.B.38: Multimodal Missing-Aware Learning for Interpretable Drug Synergy Prediction from Sparse Biological Data
• B-S.B.39: Evaluating Tumor Growth in a Glioblastoma Spheroid Model
• B-S.B.40: Integrating Network Propagation with Unsupervised Graph Representation Learning for Cancer Subtyping
• B-S.B.41: Bayesian inference of clonal fitness, age and lineage bias from snapshot single-cell hematopoiesis
• B-S.B.42: Single-cell multiome integration reveals ecDNA-associated regulatory states in lung cancer
• B-S.B.43: BLINCHESS: A Novel League-Based Approach for Differential Abundance Analysis in Case-Control Microbiome Datasets
• B-S.B.44: TEMPEH: A Computational Framework for Linking Gut Microbiome Composition and Function in Disease
• B-S.B.45: Current machine learning models fail to predict gene expression in human skin
• B-S.B.46: Identifying the Impact of Chronological Age on Pan-Cancer Profiles with Network-Modeling
• B-S.B.47: Data-driven Stratification of Asymptomatic TB Reveals Phenotypically and Molecularly Distinct Disease Subtypes
• B-S.B.48: GluSynDB: Stratifying Glutamatergic Synapse Variants
• B-S.B.49: Benchmarking 3D Alignment Methods for Spatial Transcriptomics
• B-S.B.50: Single-cell lineage analysis combined with statistical inference reveals epigenetic inheritance of a drug-tolerant phenotype
• B-S.B.51: CROssBARv2: A Unified Biomedical Knowledge Graph for Heterogeneous Data Representation and LLM-Driven Exploration
• B-S.B.52: Matrisome-Derived Gene Program Links Tumor Microenvironment to Pancreatic Cancer Progression
• B-S.B.53: MCVAE-based multi-omic anomaly detection in Fragile X Syndrome
• B-S.B.53: NELLY: A transparent deep learning framework for patient-centric drug response prediction and prioritization
• B-S.B.54: Proteomic and network analysis of Bacillus cereus adaptation: uncovering systems-level effects of a virulence megaplasmid
• B-S.B.55: Benchmarking High-Dimensional Bayesian Optimization Methods for Robust Parameter Estimation in Large-Scale Kinetic Models of Photosynthesis
• B-S.B.56: Integrative Transcriptomic Modeling of PDE3A Associated Signaling in PPGL
• B-S.B.57: Epitope Generation Gateway (EGG): A biological context dependent approach to cancer vaccine development
• B-S.B.58: scLEMBAS: Context-Aware Signaling Pathway Modeling at Single-Cell Resolution
• B-S.B.59: Integrated analysis of Visium HD spatial transcriptomics and imaging data with the nfdata-omics/spatialomics pipeline
• B-S.B.60: Exploratory classification approach for identification of rare post-infection central nervous system cell states
• B-S.B.61: Synergising Explainable AI and Multi-Omics to Unveil Heterogeneity-Defining Biomarkers in Hepatocellular Carcinoma
• B-S.B.62: Orchestrating Microbiome Analysis with Bioconductor
• B-S.B.63: Causal Machine Learning for Predictive Biomarker Discovery and Subgroup Refinement in Metastatic Colorectal Cancer
• B-S.B.64: Benchmarking temporal causal discovery for fully and partially observed biochemical kinetic models
• B-S.B.65: Electronic Health Records to Omics Latent Imputation via Marker-Aware Encoders
• B-S.B.66: Integrative Metabolomic and Microbiome Profiling Identifies Candidate Biomarkers and Pathological Mechanisms Associated with Coronary artery disease
• B-S.B.67: Mapping interactions within the tumor microenvironment that drive immunotherapy outcomes
• B-S.B.68: Recon4IMD: Leveraging UniProt, Rhea, and SwissLipids to develop improved human metabolic models for inherited metabolic diseases
• B-S.B.69: Integrative Multi-omics Analysis Reveals a Coordinated Molecular Signature of Neuroresilience in MAPT-knockout hiPSC-derived Neuron
• B-S.B.70: A Hybrid Approach Combining Machine Learning and Mechanistic Modelling for the Reconstruction of Hematopoietic Trajectories by cSTAR Reveals Novel Leukemia Differentiation Strategies
• B-S.B.71: Building a high-throughput method for computationally predicted adverse outcome pathways leading to Parkinson disease
• B-S.B.72: Integrated Plasma Multi-Omics Profiling for Predictive Biomarker Discovery for Treatment Response in NSCLC
• B-S.B.73: On paying attention to gene regulations
• B-S.B.74: Trajectory inference for flow cytometry data using TimeFlow 2
• B-S.B.75: Can AI detect people most in need of a blood-based checkup? Development and prospective validation of AI models for the prediction of future abnormal clinical laboratory tests in the Finnish population.
• B-S.B.76: Pan-cancer multimodal integration in iCAN reveals novel tumor archetypes
• B-S.B.77: Predicting Rheumatoid Arthritis Flare Risk to Guide Treatment Tapering Recommendations Across Multiple Data Modalities Using Explainable AI
• B-S.B.78: Linking transcriptomic pathway activity to tissue architecture in the endometrium using graph neural networks
• B-T.01: Scaling Scirpy for immune-cell receptor analysis of millions of single cells
• B-T.02: Tumor-immune crosstalk analysis in a pan-cancer setting to investigate tumor-driven NK cell dysfunction
• B-T.03: Inferring metabolite-mediated neuron-glia communication from single-cell transcriptomics with BrainChat
• B-T.04: Unraveling Synergistic Effects of Herpesviral Proteins on Host Polyadenylation
• B-T.05: The generation of cell type-specific gene regulatory networks from bulk RNA sequencing data
• B-T.06: Benchmarking Interventional Causal Structure Learning for Gene Regulatory Network Inference
• B-T.07: Paired gene regulatory network analysis reveals suppression of interferon signaling in metastatic breast cancer
• B-T.09: Integration of single-cell and bulk multi-omics data to identify biomarkers associated with the response to pembrolizumab in melanoma
• B-T.10: celltypeEnrich: a consensus-based scRNA-seq cluster annotation tool
• B-T.11: Differences of ex vivo and in vivo experimental models reveal challenges for AI-based perturbation prediction tools
• B-T.12: Understanding and exploiting CDK12-inactivation induced oncogenic androgen receptor signalling
• B-T.13: Shaping the Latent Space with Ordinal Losses for Dose-Response Single-Cell Transcriptomics
• B-T.14: Predicting riboswitches using deep learning
• B-T.15: Spatial characterization of prognostic prostate cancer gene signatures in targeted biopsies
• B-T.16: A Computational Framework for Comparative Analysis of Ribosome Profiling Data Across Species
• B-T.17: Computational Framework for Single-Cell Analysis and Patient-Donor Deconvolution in Thalassemia Bone Marrow Transplantation
• B-T.18: Benchmarking Tools for the Event-Level Detection of Alternative Splicing from Short-Read Data
• B-T.19: Detecting differential translation efficiency from joint modeling of spatial transcriptomics and translatomics data
• B-T.20: Regulated Transcriptional Noise in Development and Disease
• B-T.21: Investigating genomic mechanisms preventing spurious innate immune activation
• B-T.22: A lightweight deep learning model of transcription start sites in human skeletal muscles
• B-T.23: Robust Explainable Regression for Noisy Omics via a Rank-Based Algorithm
• B-T.24: ICEPS for fast and robust detection of spatially variable genes
• B-T.26: Species-specific oxygen sensing governs the initiation of vertebrate limb regeneration.
• B-T.27: Integrative Bioinformatics to Identify Dynamic Transcriptional and Post-Transcriptional Controls.
• B-T.28: Multi-modal profiling uncovers a MYC-MIZ1-lysosomal axis controlling immune exclusion in pancreatic cancer
• B-T.29: Combining massively parallel reporter assays and graph genomics to fine-map regulatory variants in cattle
• B-T.30: IL-15 Drives Superior Th17-like Polarization and Cytotoxic Potency in iNKT Cells: A Multi-Workflow Single-Cell Validation Study
• B-T.31: What you express and how you split it: transcript isoform usage carries drug-response signal in cancer cell lines that current expression-based models do not capture
• B-T.32: Single cell transcriptomics identifies male selective vulnerability to estrogen receptor beta loss across amyloid, glial, and vascular pathways in the AppNL-G-F mouse model of Alzheimer’s disease
• B-T.33: Decoding Regulatory Mechanisms of RNA-Binding Proteins within Coding Sequences
• B-T.34: Order Matters: Ambient RNA correction affects doublet detection in single-cell
• B-T.35: Assessing Metadata Extraction for Identifying Compatible Transcriptomics Experiments in GEO
• B-T.36: Bambu-Pipe: a nextflow pipeline for multi-sample long-read single-cell & spatial transcriptomics data
• B-T.37: Reproducible Cell-Type Specific Coexpression Patterns
• B-T.38: In-depth spatial characterization of the Wilms tumor microenvironment
• B-T.39: Decoding chronic inflammatory skin diseases using scRNA-seq
• B-T.40: Super-Resolution Modeling of 4sU Labeling Data Enhances Temporal Resolution
• B-T.41: Inference of sample-specific gene networks via decision path mapping in random forests
• B-T.42: Proteomics reveals inflammatory states and highlights potential for plasma-based identifica-tion of neuroborreliosis in children with facial palsy
• B-T.43: SegmentQTL: uncovering allele-specific genetic regulation underlying chemotherapy resistance in ovarian high-grade serous carcinoma
• B-T.44: 3D human bioengineering and transcriptomics to generate high-fidelity models of doxorubicin-cardiotoxicity
• B-T.45: Integration of transcriptomic technologies to improve the maturation and biomimetism of human engineered heart tissues.
• B-T.46: Assessment of omics-based biases in pathway analyses using ReactomeGSA
• B-T.47: Sparse Autoencoders Reveal Interpretable Features in Single-Cell Foundation Models
• B-T.48: Beyond Bulk: How 3’ UTR length and alternative splicing can advance bulk transcriptomic analysis in tuberculosis
• B-T.49: Parnet: a protein-RNA interaction-based RNA foundation model for functional prediction and therapeutic design
• B-T.50: ASAP: a portal for reproducible single-cell dataset analyses
• B-T.51: Generative AI for rational design of cell type-specific UTRs
• B-T.52: Spatial gene regulatory networks
• B-T.53: Comparison and optimization of cellular neighbor preference methods for quantitative tissue analysis
• B-T.54: spDDB: Comprehensive benchmarking of spatial deconvolution and domain detection methods for spatial transcriptomics datasets across technologies and tissue types
• C-B.01: Metabolic modeling reveals microbial metabolites associated with infant temperament traits
• C-B.02: Deep Orthogroups: Extending Orthogroups beyond the root of the species tree.
• C-B.03: Genome-Centric Characterisation of Limnochordia in Biogas Microbiomes: From Phylogenetic Diversity to Clade-Specific Functions
• C-B.04: Unmasking the Growing Challenge and Hidden Impact of Sampling Bias in Prokaryotic Genome Collections
• C-B.05: FlaPro: a computational framework for TLR5-phenotype-aware flagellome profiling across human gut metagenomes and metatranscriptomes
• C-B.06: Fast, flexible gene cluster family delineation with IGUA
• C-B.07: MetagenomeWatch: Web-based monitoring and search of public metagenomic data for pathogen surveillance
• C-B.08: Comparative methods for RNA Secondary Structure Prediction in RNA Viruses
• C-B.09: ABRomics: a platform for antibiotic resistance research and public health using an integrated One Health approach
• C-B.10: Phylogenetic ordering and batching for better compression of million-genome bacterial collections
• C-B.11: A research-oriented framework for viral discovery and interpretation in metagenomic data
• C-B.12: Characterization of Microbial Succession During Spontaneous Fermentation Using Long-Read Amplicon Sequencing
• C-B.13: Plant species pool size is a better predictor of arbuscular mycorrhizal fungal diversity and biomass than local plant species richness
• C-B.14: ViHostFinder: A Hierarchical Multilabel Host Predictor for Viral Sequences Using DNA Language Models
• C-B.15: Inter-individual variation in gut microbiome composition impacts identification of tissue type and cancer status in colorectal cancer
• C-B.16: ANETO: A Host-Agnostic Platform for Microbiome and Multi-Omics Discovery
• C-B.17: Consistent, scalable prokaryotic genome annotation and clustering to support biodiversity representation across EMBL-EBI resources
• C-ELIXIR.01: From tools to interoperable workflows for nucleic acid structures: the NA Hackathon 2026 as an ELIXIR community model
• C-ELIXIR.02: ELIXIR IMPACT: a Swiss-Czech partnership for Integrating Molecular, Pathogen And Clinical data with AI Technologies
• C-G.01: Adaptive Dynamics of HIV-1 Populations Over a Six-Year-Long Experimental Evolution: A Genomic Perspective
• C-G.02: Decoding the noncoding: a computational and CRISPR-enabled framework from long noncoding RNA discovery to therapeutic targeting
• C-G.03: Predicting Genomic Determinants of Chromatin Compaction Using Machine Learning
• C-G.04: GPxLMM: Gaussian Process-Augmented Linear Mixed Models for Genotype-by-Environment Interaction Analysis
• C-G.05: nf-core/raredisease: Modernizing Clinical Genomics by Transitioning to Community-Driven Workflows
• C-G.06: Role of histone H2A lysine119 ubiquitination in gene regulation in BAP1-positive and BAP1-negative uveal melanoma eye cancer.
• C-G.07: Imputation of cgMLST Profiles from Bacterial Genomes
• C-G.08: A systematic investigation into the robustness of mutational signature fitting to unknown signatures
• C-G.09: Exploring twin genes with DupyliCate
• C-G.10: Cracking the animal venom code using phylogenetic big data
• C-G.11: MosCoverY: A method to estimate mosaic loss of Y chromosome from sequencing coverage data
• C-G.12: Computational Detection of Methylated Bases in Bacteria: PacBio vs. ONT
• C-G.13: High-confidence Structural Variants without Truth Set from Cattle Long Reads
• C-G.14: Neural posterior estimation for population genetics
• C-G.15: Extending the megSAP pipeline for medical genetics to long-read sequencing
• C-G.16: MtDNA Variant and Heteroplasmy Profiling in Patients with COPD
• C-G.17: Not All Out-of-Distribution Is Created Equal: Reasoning Demand in Perturbation Prediction
• C-G.18: Automated cell type prediction in Mass Cytometry data using machine learning with interactive web-based annotation review
• C-G.19: Management of Large Variant Datasets
• C-G.20: DNA methylation changes in genomic regulatory blocks in head and neck cancer
• C-G.21: Decoding Heritable Breast Cancer Predisposition through Integrative Gene-Based Pathway Analysis
• C-G.22: SynVar: extending variant query expansion to GA4GH-compliant variant normalisation for literature annotation
• C-G.23: A Bi-partite Graph Neural Network for Polygenic Risk Scoring under the Omnigenic Model
• C-G.24: Domain-wide Mapping of Peer-reviewed Literature for Genetic Developmental Disorders using Machine Learning and Gene2Phenotype
• C-G.25: AIOMICS4CARE WGS: AI enabled Annotation System for Whole Genome Sequencing Data
• C-G.26: Searching for bacterial capsule at scale
• C-G.27: Benchmarking Pathogenicity Estimation Methods with Real-World Clinical Data in Inherited Heart Disease
• C-G.28: Identification of conserved gene clusters across diverse prokaryotic pangenomes
• C-G.29: Epigenomic Instability - Is Epigenetic Age Acceleration a Survival Indicator in Lung Cancer?
• C-G.30: Unveiling the functional fate of duplicated genes through expression profiling and structural analysis
• C-G.31: Integrative gene network analysis of genome-wide association data in myalgic encephalomyelitis / chronic fatigue syndrome
• C-G.32: Evolutionary Conservation and Functional Constraints of TP53 Mutation Hotspots Across Mammalian Species
• C-G.33: PanTEon: a cross-kingdom framework to guide the design of transposable element classifiers
• C-G.34: DeepCAST-GWAS: Improving the Discovery of Genetic Associations Using Deep Learning-Based Regulatory SNP Prioritization
• C-G.35: Improving genomic prediction through agentic AI and DNA foundation model SNP selection
• C-G.36: Managing workflow executions with WESkit
• C-G.37: Quantitative Modeling of Clone-Specific Treatment Resistance via Joint Bayesian Inference of Compositional and Population Size Data
• C-G.38: PansimNuc: Rapid nucleotide-level simulation of selection and genetic element mobility in eukaryote pangenomes
• C-G.39: Expanding Gene Ontology coverage of the human functionome in the PAN-GO project
• C-G.40: From DMPs to DMRs: CMEnt, Characterization of Methylation using positional ENTanglement
• C-G.41: Computel 2.0: A Platform for Comprehensive Telomere Profiling and TRV Phenotyping
• C-G.42: From ageing clocks to human digital twins in personalising healthcare through biological age analysis
• C-G.43: Annotating Eukaryotic Genomes by Combining Deep Learning with Extrinsic Evidence
• C-G.44: Convergent niches, divergent genomes: a four-species pan-GWAS of Aspergillus pathogenicity and domestication
• C-G.45: Gemsparcl: Rapid and consistent clustering of millions of genomes highlights the diversity of prokaryotic life
• C-G.46: Whole-organism sequence-to-function modelling stratifies the cis-regulatory code of Drosophila into enhancer and locus grammars
• C-G.47: MoleMap: fast alignment-free molecule mapping for long-read and linked-read sequencing data
• C-G.48: Federated Genomic Variant Discovery & Analysis Using GA4GH Standards
• C-G.49: Large-scale genetic analysis of healthcare expenditure in 1.4 million individuals: The GenCost Consortium
• C-G.50: High-Resolution Characterization of Repeat Expansions in Friedreich's Ataxia by Targeted Long-Read Sequencing
• C-G.51: Design and Validation of Deep Learning Models for Decoding Cis-Regulatory Logic in Drosophila melanogaster
• C-G.52: Comparative benchmarking of DNA methylation imputation methods in the human placenta
• C-G.53: Polygenic scores derived from breed-level obesity traits predict food motivation in individual dogs
• C-G.54: Genomic insights into racing camels: inbreeding levels and positive selection linked to athletic traits
• C-G.C.01: Invariom-Derived Geometric Priors Improve Conformer Generation Beyond Crystallographic Chemical Space
• C-G.C.02: IDEAL-GENOM: A Python toolkit for automation of genomic analysis
• C-G.C.03: Global antimicrobial resistance patterns in human gut metagenomes are structured along socio-economic gradients
• C-G.C.04: Active Learning-Guided Docking for Efficient Discovery of LasR-Targeting Anti-Virulence Compounds for Diabetic Foot Infections
• C-G.C.05: Grounding AI Agents in Viral Genomics: A Deterministic Retrieval Layer for Reliable Autonomous Surveillance
• C-G.C.06: Exploration of Epitranscriptomic's Landscape through Self-Supervised Language Models
• C-G.C.07: Hecate: A Modular Genomic Compressor
• C-G.C.08: Automated Machine Learning to Identify Drivers of Microbial Community Composition
• C-G.C.09: Rotavirus A genotype diversity and antigenic profile in Central Ethiopia: implications for Rotarix® vaccine efficacy
• C-G.C.10: Population structure of wild and cultivated grapevines in Armenia
• C-G.C.11: FuFisOr Finder: A Bioinformatics Pipeline for Detecting Orthology Based Fusion and Fission Events in Protein Sequences
• C-G.C.12: OncoSpat: A Harmonized Pan-Cancer Spatial Transcriptomics Atlas for Quality-Aware Exploration of Tumor Architecture
• C-G.C.13: Influence of Peptide-HLA Binding Modes, Different HLA Alleles, and Epitope Mutations on TCR Recognition
• C-G.C.14: Copy number aberrations. The challenge of WES, FFPE, tumor only samples.
• C-G.C.15: Analysis of current needs, barriers, and formats for computational life sciences training - Swiss and global views.
• C-G.C.16: Depictio: an open-source platform for building interactive dashboards from bioinformatics workflow outputs
• C-G.C.17: Fostering Sustainability and Environmental Awareness at Work
• C-G.C.18: AD-CLIP: Adenylation Domain-Substrate Contrastive Learning for Virtual Screening
• C-G.C.19: LD-Attention: A Generalized Linkage Disequilibrium Aware Attention Layer for Transformer-Based Genetic Analysis
• C-G.C.20: Robust Estimation of SARS-CoV-2 Lineage-Specific, Confirmed Case Burden Under Limited Sequencing Using a Bayesian Framework
• C-G.C.21: Oligo Designer Toolsuite - lightweight development of custom oligo design pipelines
• C-G.C.22: Wastewater surveillance of endemic human coronaviruses in Switzerland
• C-G.C.23: Predicting Molecular Hotspots for Efficient Drug Discovery using Deep Learning
• C-G.C.24: Translating omics data into discovery: the Biomedical Data Science Facility
• C-G.C.25: The MicrobeAtlas database in 2026: an expanded view of the global microbial ecosphere
• C-G.C.26: Tumour evolution as ground truth for cancer whole-genome sequencing
• C-G.C.27: CLUES A Comprehensive Workflow for Integrating Geospatial Data in Biomedical Research
• C-G.C.28: Geometry of Antigenic Space Enables Optimization of HCV Vaccine Antigen Design
• C-P.01: Interpreting the function of germline missense variants for the BAP1 gene using protein centric information.
• C-P.02: Generalizable Protein Language Models for Complex Variant Prediction via Multi-Task Epistatic Learning
• C-P.03: Do AlphaFold Ensembles Capture Side-Chain Fluctuations? A Large-Scale Benchmark Against MD and PDB Homologs
• C-P.04: Benchmarking structure prediction in the time of AI
• C-P.05: LLM-Assisted Development of Large-Scale Proteomics Infrastructure: An Empirical Study from PRIDE Reanalysis
• C-P.06: Amyloid Landscape Explorer: A Network-Based Platform for Thermodynamic and Structural Comparison of Amyloid Fibrils
• C-P.07: The Encyclopaedia of protein Domains (TED) from AlphaFold2-predicted structures: expansion of CATH domain space and insights into structural diversity
• C-P.08: Iterative HMM-Refined Coevolution Analysis Combined with AlphaFold3 for Protein Complex Discovery
• C-P.09: From 1,000 Spiders to Designed Protein Materials: Decoding the Spider Silkome
• C-P.10: LIGYSIS: a resource for predictor training, benchmarking and functional characterisation of ligand binding sites
• C-P.11: 3DSeqCheck: Identifying discrepancies of structure-based resources such as AlphaFoldDB in relation to the evolving UniProt entries
• C-P.12: Rewriting protein alphabets with language models
• C-P.13: Predicting changes in protein-protein binding affinity upon mutation with statistical potentials
• C-P.14: AI Enhanced Viral Structural Phylogenetics
• C-P.15: MotifCraft: scalable motif scaffolding and protein binder design for large protein targets
• C-P.16: ProInterVal-BioXtal: A Web Server to Distinguish Biological Interfaces from Crystal Contacts Using Graph Deep Learning
• C-P.17: Improving protein structure prediction with structure-aware multiple sequence alignments
• C-P.18: Computational prediction of ligand-receptor pairs using transformer-based language models, interaction networks, and structural modelling
• C-P.19: Generating tailored high-quality datasets for benchmarking structure-based computational drug design tools: a docking assessment
• C-P.20: Integrated CSF-serum proteomic profiling in spinal cord injury
• C-P.21: Exploring the mobile genetic element continuum using PhageProfiler
• C-P.22: Exploring protein language model approaches for thermal stability prediction
• C-P.23: A Unified Framework for TCR-pMHC Structural Model Assessment
• C-P.24: From Proteome Profiles to Clinical Diagnosis in Children with Bone and Joint Infections
• C-P.25: PUFFIN: Protein Unit Discovery with Functional Supervision
• C-P.26: PPInterface v2: A Dataset and Web Resource for 3D Protein–Protein Interface Analysis and Visualization
• C-P.27: RECUR: identifying recurrent amino acid substitutions from multiple sequence alignments
• C-P.28: Machine Learning Classification and Analysis of Gain-of-Function, Loss-of-Function, and Neutral Mutations in Cancer
• C-P.29: AI-driven Intrabody Design: Representation Learning and Multi-objective Optimization of Nanobody Frameworks
• C-P.30: Identification and characterization of Polyurethane-Degrading Enzymes from MGnify Metagenomes
• C-P.31: EnzymeSifter: a pipeline for identifying and filtering industrial enzymes from metagenomes across multiple predicted biochemical properties
• C-P.32: High Diversity Gene Libraries Facilitate Machine Learning Guided Exploration of Fluorescent Protein Sequence Space
• C-P.33: Deep generative modeling captures maturation-dependent pairing patterns in human antibodies
• C-P.34: Enhancing protein-ligand binding site predictions: Integrating protein language models with geometric smoothing and clustering
• C-P.35: From PMGen to PMpan: Fast and Accurate Peptide-MHC Modeling, Sampling and Binding Prediction
• C-P.36: Mapping targetable sites on the human surfaceome for the design of novel binders
• C-P.37: Mitigating Functional Classification Hallucination in Protein Language Models Through Target-Decoy Training
• C-P.38: Assessing the stability and oligomerisation of a β-hairpin through gas-phase molecular dynamic simulation
• C-P.39: Breaking the Data Bottleneck: Leveraging Transfer Learning for data-scarce Post-Translational Modifications
• C-P.40: Plant Biocuration in UniProtKB/Swiss-Prot
• C-P.41: Generating and evaluating coevolution and phylogeny-aware MSAs
• C-P.42: AI-driven structural modeling reveals hidden functional and evolutionary relationships in divergent dsRNA viruses
• C-P.43: The sequence-structure landscape of antibody framework regions
• C-P.44: Dynamic Behavior of the RAG2 Acidic Region and Its Possible Functional Significance
• C-P.45: Mapping Evolutionary Switches Driving Functional Diversification in AsnC-like Transcription Factors
• C-P.46: Residue-Level Attributions in Protein Language Models Do Not Recover Allergen Epitopes
• C-P.47: Computational search for Myelin-associated Glycoprotein (MAG) binders that could modulate the neuroprotective properties of oligodendrocytes in neurodegenerative contexts
• C-P.48: A novel analysis pipeline for scFv discovery from long-read platforms
• C-P.49: Investigating Enzyme Function by Geometric Matching of Catalytic Motifs
• C-P.50: Unveiling Recurrent Binding Sites in H1N1 Nucleoprotein via Ensemble-Based Pocket Clustering
• C-P.51: Tokenization-Aware Protein Language Modeling with Evolution-Guided Units and Dynamic Biological Knowledge Fusion
• C-S.B.01: Notation variance in chemical language models: effects of inconsistent SMILES on representation stability and benchmark evaluation
• C-S.B.02: Integrative multi-omics analysis identifies a phenol-associated microbe-metabolite-host interaction in MASLD
• C-S.B.03: Data-Driven Disentanglement of Confounding Factors in Sjögren's Syndrome
• C-S.B.04: AI-driven classification of signaling proteins: histidine kinases as a case study
• C-S.B.05: Towards Comparative QTLomics
• C-S.B.06: TopOmics: Topic Modelling for all -Omics
• C-S.B.07: MetaNetX: enhancing metabolomics data integration through comprehensive reconciliation
• C-S.B.08: Integrating tumour evolutionary patterns with genomic and transcriptomic signatures improves patient stratification in metastatic prostate cancer.
• C-S.B.09: A Unified Single-Cell Atlas of Mouse Vascular Disease: From Integration to Interactive Exploration with AtlasLens
• C-S.B.10: Evaluating and Improving Optimal Transport for Temporal Single-Cell RNA-Seq Data.
• C-S.B.12: A modular RDF schema for multi-source biomedical data integration: application to Crohn's disease
• C-S.B.13: A Large-Scale Resource for Single-Cell H&E and Spatial Transcriptomics Reveals the Importance of Tissue-Specific Learning
• C-S.B.14: Optimized Clustering of Patients with Type 2 Diabetes (T2D): Mining Data from Population-Based Cohort Studies
• C-S.B.15: Systematic capture of human receptor-ligand interactions as Gene Ontology Causal Activity Models
• C-S.B.16: Protein abundance inference at single-cell resolution
• C-S.B.17: Identifying Functional ROIs for Spatial Transcriptomics from H&E Images via Pathology Foundation Models
• C-S.B.18: jsPCA: fast, scalable, and interpretable identification of spatial domains and variable genes across multi-slice and multi-sample spatial transcriptomics data
• C-S.B.19: Deciphering context-specific protein interactomes to uncover protein functions and therapeutic targets
• C-S.B.20: Deciphering Disease Transcriptional Logic by Revealing TF Composite Modules
• C-S.B.21: KidsCan Analytical Pipelines - A Multi-Omics Framework for Pediatric Precision Oncology in Switzerland
• C-S.B.22: Multimodal Deep Learning for Predicting of RNA Subcellular Localization
• C-S.B.23: A Deep Generative Framework for Joint Modeling of Single-Cell Isoform Expression and Full-Length Transcript Sequences
• C-S.B.24: Topology-aware reconstruction of cellular state landscapes from microscopy using self-supervised learning
• C-S.B.25: To Translate or Not: Multistable Decision-Making in Translation Initiation under Normal Conditions and Integrated Stress Response
• C-S.B.26: Structured dimensionality reduction for refining coding-agent-authored single-cell embedding models
• C-S.B.27: Specifying single-cell data simulation tasks for LLM agents to contrast literature knowledge with real data
• C-S.B.28: From trial-and-error to precision therapy: A multi-omics knowledge-graph framework for primary immune regulatory disorders
• C-S.B.29: Phenotype-driven parallel embedding for microbiome multi-omic data integration
• C-S.B.30: MUSE enables cross-species multi-omics integration that incorporates transcriptional regulatory modules
• C-S.B.31: MultiOmics Centre: a driver of innovation in food, agriculture and health
• C-S.B.32: OXidative Stress PREDictor: A Supervised Learning Approach for Annotating Cellular Oxidative Stress States in Inflammatory Cells
• C-S.B.33: Deciphering signal propagation in the context of an activating cyclin dependent kinase 4 mutation
• C-S.B.34: Sensitivity and correlation analysis of Calvin-Benson cycle models identifies key parameters and reduced effective dimensionality
• C-S.B.35: Comprehensive reannotation of human enzymes and transporters in Swiss-Prot using Rhea
• C-S.B.36: Phototransduction in the visual system as an example of biological system-based curation of protein function in Swiss-Prot
• C-S.B.37: Decoding spatial niche architecture of dedifferentiation in aggressive thyroid cancer
• C-S.B.38: Data-driven subgrouping of individuals on Alzheimer’s continuum based on amyloid-β aggregation
• C-S.B.39: MorphoMapper: 3D Segmentation-Driven Feature Mapping of Stress-Perturbed Mitochondrial Remodeling
• C-S.B.40: Interactive Interfaces for Comprehensive Genetic Evidence in Target Identification: The Open Targets Platform
• C-S.B.41: Using predictive multiplicity in biologically informed neural networks to uncover disease heterogeneity
• C-S.B.42: Assessing the relative contributions of mosaic and regulatory developmental modes from single-cell trajectories
• C-S.B.43: LGTM: Gaussian Process Modulated Neural Topic Modeling for Longitudinal Microbiome
• C-S.B.44: GPU-accelerated WGCNA for scalable gene co-expression analysis
• C-S.B.45: Random-walk multi-omics integration for rare-disease gene prioritization
• C-S.B.46: Integrating LINCS L1000 transcriptomic repurposing with graph convolutional drug-target interaction prediction identifies subtype-specific therapeutic candidates and novel targets in IBD
• C-S.B.47: MicroKnow: Quantifying the Mechanistic Evidence Gap in Clinical Microbe-Disease Associations
• C-S.B.48: Delineation of signaling routes that underlie differences in macrophage phenotypic states
• C-S.B.49: MetaboViz: A Zero-Install Three-Tier Browser Platform for Interactive Genome-Scale Metabolic Modelling
• C-S.B.50: Multimodal integration reveals immune mechanisms of an effector consortium
• C-S.B.51: Microglia as transcriptional pacemakers of neuronal aging heterogeneity across individuals
• C-S.B.52: A Computational Framework for Cross-Species Single-Cell Atlas Integration Reveals Conserved and Divergent Transcriptional Programs in Tongue Pain Circuits
• C-S.B.54: Multimodal Siamese Network for Parkinson's Disease Diagnosis and Structure-based Drug Discovery for Therapeutic Development
• C-S.B.55: AgroLD: a knowledge graph for the plant sciences
• C-S.B.56: Core genome translational genomics - A framework to combine multi species data for the understanding of complex traits
• C-S.B.57: Harnessing AI to Identify Key Microbial Drivers of Stable State Microbiomes in Low Emitting Ruminants
• C-S.B.58: ToxCast Evidence Graph: Portable Semantic Access to Bioactivity and Product-Use Context
• C-S.B.59: Ecoli-GEM, an updated and standardized consensus genome-scale metabolic model, shows that E. coli is proton-saturated
• C-S.B.60: Inferring somatic gene expression evolution from single-cell data
• C-S.B.61: Microneedle-Based Salivary Lactate Monitoring for Model-Assisted Assessment of Exercise Intensity and Overtraining Risk
• C-S.B.62: A Probabilistic Digital Twin Framework for Uro-Oncology: In Silico Modeling and Virtual Experimentation of the FGFR3 Pathway
• C-S.B.63: Automated lesion segmentation enables large-scale analysis of heterogeneity in multiple sclerosis
• C-S.B.64: Ion Channel Degeneracy Underlies Pacemaker Identity: Large-Scale Computational Evidence from a Biophysically Constrained Hodgkin-Huxley Framework
• C-S.B.65: Cluster-Aware Functional Principal Component Analysis for Imputing Missing Values in Longitudinal Microbiome Data
• C-S.B.66: Characterizing the Clinical and Functional Correlates of Age of Onset in 5876 Mendelian Diseases
• C-S.B.67: A Biochemical Reaction Network Model of Autophagy-Apoptosis Crosstalk Under Metabolic Stress
• C-S.B.68: Mutation-driven reorganization of proteomic covariation networks
• C-S.B.69: Joint Representation Learning and Graph Construction for Multimodal Patient Similarity Networks
• C-S.B.70: Deep Learning for BioImaging: What Are We learning ?
• C-S.B.71: β-Cell State Transitions Reveal Metabolic Rewiring and Adaptive Stress Programs in Type 2 Diabetes
• C-S.B.72: Critical Assessment of Metagenome Interpretation: Round Three of Metagenomic Software Benchmarking Challenges
• C-S.B.73: Module Graph: A Web-Based Framework for Module-Centred Metabolic Network Analysis: Bridging KEGG Orthologs and Functional Metabolic Organisation
• C-S.B.74: Risk-averse optimization of genetic circuits under uncertainty
• C-S.B.75: From Code to Response: A Modular Data Infrastructure for Longitudinal SARS-CoV-2 Immune Surveillance
• C-S.B.76: Systematic Discovery of Alternative Splicing–Derived Neoepitopes Using Integrative MHC Presentation and Immunogenicity Prediction
• C-S.B.77: A leakage-aware benchmark for gene prioritization across feature representations and integration strategies
• C-S.B.78: Novel Software to Analyze Long-term Potentiation Recordings
• C-T.01: Single-cell long-read genotyping reveals subclonal homologous recombination gene reversions in high-grade serous ovarian carcinoma
• C-T.02: Computational Identification of Structural Switches in Super-enhancer-derived lncRNAs as Drivers of Glioma Progression
• C-T.03: A novel machine learning framework for optimized prediction of cardiac regulatory element activity
• C-T.04: Do sequence-to-activity models dream of larger contexts?
• C-T.05: An Adaptation of the Gini Index for Cell Type Specificity in Single Cell Data: Application to lncRNAs
• C-T.06: Transcriptomic, epigenomic and cis-regulatory variation in testis tissue of pre- and postpubertal bulls
• C-T.07: A Spatial Transcriptomic Approach to understanding to the role of FAPs in Duchenne Muscular Dystrophy
• C-T.08: Split-flow enables concordance-based demultiplexing of pooled single-nucleus multiome data for multi-layered analysis of AML therapy response
• C-T.09: Comprehensive Transcriptome Analysis of Prostate Cancer Reveals Isoform-Specific Associations with Cancer Pathways
• C-T.10: A study of the genetic circuits of intestinal stem cell differentiation using in vivo CRISPR perturbation and single-cell RNA-seq
• C-T.11: GOTFlow: Learning Directed Population Transitions from Cross-Sectional Biomedical Data with Optimal Transport
• C-T.12: A harmonized multi-study single-cell atlas of psoriasis skin reveals cellular and molecular remodeling across disease and treatment states
• C-T.13: An integrated computational framework for identification of differential cell-cell interactions in multi condition single cell studies
• C-T.14: Developing a gene regulatory network-based predictor of drug response and outcome in cancer
• C-T.15: African Swine Fever – Dynamics of infected macrophage populations
• C-T.16: Quantifying Ligand Diffusion and Cell–Cell Communication at Single-Cell Resolution
• C-T.17: Ten common mistakes that could ruin your enrichment analysis!
• C-T.18: Gene-Anchored Contrastive Regulatory Embedding
• C-T.19: Foundation Model-Guided Cell Type Annotation for Imaging-Based Spatial Transcriptomics
• C-T.20: T2DIAbetes (Type 2 Diabetes Integrated Atlas): A web-based tool to study Type 2 Diabetes using tissue-specific atlases
• C-T.21: MARGIN: tuMour-Anchored ReGional INference for spatial enrichment at the tumour-stroma boundary
• C-T.22: Cell-type-specific alterations in transcriptional noise across brain regions in Alzheimer's disease
• C-T.23: Spatial transcriptomic contextualization of candidate therapeutic targets across inflammatory and fibrotic stages of experimental autoimmune myocarditis
• C-T.24: Fold-change-specific pathway enrichment analysis and its application to human breast cancer cells treated with a novel carbonic anhydrase inhibitor
• C-T.25: Recurrent intra-tumour heterogeneity is a hallmark of metastatic prostate cancer
• C-T.26: Deciphering the Adrenergic-to-Mesenchymal Transition in Neuroblastoma through Gene Regulatory Network Analysis
• C-T.27: A database of reference datasets for the annotation of single-cell and spatial omics
• C-T.28: RNA Language Models enable accurate, scalable and interpretable modification prediction
• C-T.29: Bioinformatic analysis of A-to-I RNA editing in inverted alu pairs within 3' UTRs
• C-T.30: Exploring the mechanism for temporal regulation of gene expression during Drosophila embryogenesis
• C-T.31: SULALA: Explainable Machine Learning for Identifying cis-Regulatory Elements Across Developmental Trajectories
• C-T.32: Exploring tumor-suppressing fibroblast population in ovarian adenocarcinoma
• C-T.33: Decoding Translation Initiation with Massively Parallel 5'UTR Reporter Assays and Interpretable Machine Learning
• C-T.34: miREA: a network-based tool for microRNA-oriented enrichment analysis
• C-T.35: Transcriptomic Profiling Identifies Immunotherapy-Responsive Phenotypes in Microsatellite-Stable Metastatic Colorectal Cancer
• C-T.36: Genedex: a computational framework for unifying Alzheimer's Disease signatures
• C-T.37: PCAGroupAdam: A PCA-Based Deep Learning Framework with Custom Optimization for Cancer Biomarker Discovery and Classification in High-Dimensional Gene Expression Data
• C-T.38: Long-read RNA sequencing unveils a novel cryptic exon in MNAT1 along with its full-length transcript structure in TDP-43 proteinopathy
• C-T.40: Are small RNA-Seq cohorts reliable? Navigating replicability and precision
• C-T.41: Automated inference of regulatory networks from high-throughput data using SwissRegulon.
• C-T.42: Cell-type- and locus-specific epigenetic editing of memory expression
• C-T.43: Decoding disease at resolution: Building and deploying single-cell long-read sequencing capabilities to accelerate drug discovery at GSK
• C-T.44: A Dual Approach to Rank RNA-Seq Pipelines with Weak Signals]{A Dual Approach to Evaluate the Performance of RNA-Seq Data Analysis Pipelines with Weak Signals
• C-T.45: Testing various CNV callers to substitute cytogenetic karyotype analysis
• C-T.46: Accurate Cell Density Estimation with Nucount Uncovers Novel Tissue Typologies in Spatial Transcriptomics
• C-T.47: Somatic Variant Identification in Monoclonal B-cell Lymphocytosis Using Single-Cell RNA Sequencing
• C-T.48: Rethinking bulk-to-single-cell transfer: Lessons from a systematic benchmark
• C-T.49: Role of the tumor microenvironment in the emergence of cancer stem-like cells in human lung adenocarcinoma
• C-T.50: A Network Story: tumor educated platelets transcriptome and Glioblastoma
• C-T.51: In Silico Prioritization of Combinatorial Regulatory Perturbations Using Gene Expression Models
• C-T.52: Multi-modal Transcriptomics Reveals Receiver–Effector Mismatch and Downstream Inflammatory Positioning of CSF2 in Psoriasis
• C-T.53: Co-Expression Transition Network Model: A Framework for Differential Co-Expression and Clustering
• C-T.54: TROP-2 transcriptomic and Microenvironment signatures predict aggressive phenotype and therapeutic resistance in metastatic melanoma
• C-T.55: DNA methylation shaping cell fate in placental development using single-nuclei multiome sequencing data
• Capturing the Diversity of Life - Reorganizing the Protein Space in UniProtKB
• Computational insights into enhanced deubiquitination by SARS-CoV-2 PLpro K232Q - A structure - function study
• REFLECT: Enhancing single-cell type classification via supervised contrastive representation learning
• Robust cross-study body fluid identification using an extreme gradient boosting classifier
• SynOmicsBench: A unified benchmark of synthetic data generation for clinical transcriptomic cancer cohorts