Analysis of single-cell transcriptome data has provided valuable insights into biological mechanisms at molecular resolution. However, learning
phenotype-informative patient-level representations from single-cell data while supporting interpretable, cell-type-resolved gene prioritization
remains challenging. Key obstacles include (i) capturing heterogeneity in cellular states and their interaction-structured molecular context over
large gene networks, (ii) learning patient-level predictors under scarce supervision despite abundant cells, and (iii) navigating a combinatorial
search space over genes and cell types for interpretable prioritization. In this work, we introduce MIL2Het, a deep learning framework released as
a comprehensive Python package for phenotype prediction and post-hoc interpretation from scRNA-seq with patient labels. Across scRNA-seq
cohorts spanning breast cancer, COVID-19, and asthma, MIL2Het showed strong and consistent classification performance. The same trained
framework further yielded biologically coherent, cell-type-resolved gene prioritization that varied with disease context and clinical setting.
Together, these results suggest that MIL2Het provides a practical framework for patient-level prediction and context-aware biomarker hypothesis
generation from scRNA-seq.

Co-authors: Sun Kim, Jeonguk Choi, Changyun Cho, Ilho Yun, Seungeun Kim, Daeun Kim, Sujin Seo, Sungho Won, Taebum Kim