{"id":1871,"date":"2022-05-25T22:13:00","date_gmt":"2022-05-25T22:13:00","guid":{"rendered":"https:\/\/eccb2022.org\/?page_id=1871"},"modified":"2022-07-05T07:33:26","modified_gmt":"2022-07-05T07:33:26","slug":"ntb-w10","status":"publish","type":"page","link":"https:\/\/eccb2022.org\/ntb-w10\/","title":{"rendered":"NTB-W10"},"content":{"rendered":"\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-6f6b038\" data-block-id=\"6f6b038\"><style>.stk-6f6b038 .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>Title<\/strong><\/span><\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-be6eed2\" data-block-id=\"be6eed2\"><h3 class=\"stk-block-heading__text\">Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning<\/h3><\/div>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-77074a6\" data-block-id=\"77074a6\"><style>.stk-77074a6 .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #1a928e);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">Workshop details<\/span><\/span><\/span><\/strong><\/span><\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<ul><li>Date: Sunday, September 18th<\/li><li>Time: 14:00 to 18:00 CEST (Slot 26)<\/li><li>Format: Face-to-face<\/li><li>Room: TBD<\/li><\/ul>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-0994530\" data-block-id=\"0994530\"><style>.stk-0994530 .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>Organisers<\/strong><\/span><\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<ul><li><strong>Mar\u00eda Rodr\u00edguez-Mart\u00ednez<\/strong><\/li><li><strong>Anna Niarakis<\/strong><\/li><li><strong>Matteo Barberis<\/strong><\/li><\/ul>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-4a8e43e\" data-block-id=\"4a8e43e\"><style>.stk-4a8e43e .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\">Topic<\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<p>Interpretable machine learning &amp; statistical modelling of the immune system in health and disease settings<\/p>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-4cdfd01\" data-block-id=\"4cdfd01\"><style>.stk-4cdfd01 .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\">Abstract<\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<p>The immune system is highly complex, and its malfunctioning can result in a wide number of disorders. A correct understanding of its inner workings is crucial to design optimal immunotherapies, develop new vaccines, understand the molecular basis of autoimmune diseases, etc. However, immune-related diseases pose specific challenges associated with the incomplete understanding of the underlying highly non-linear molecular and cellular interactions, and with the limited therapeutic options available based on existing drugs.<\/p>\n\n\n\n<p>Recent years have witnessed the unstoppable development of high-throughput experimental technologies in molecular biology. We can now routinely profile thousands or even millions of single cells and millions of profiles are already publicly available. The availability of large amounts of molecular data has powered the development of many statistical and machine learning models focused on understanding the complexity of the immune system. Statistical and machine learning approaches have long been considered orthogonal approaches, the former being focused on modelling statistical relationships, and the latter on identifying hidden data patterns and correlations. However, the synergy between both approaches may greatly improve the accuracy and coverage of computational models, and help to disentangle the complex mechanisms that govern immune processes.<\/p>\n\n\n\n<p>Attention will be given to the rising field of interpretable deep learning, which aims to overcome the black-box nature of most currently available deep learning models. Lack of understanding\u2014inherent to black-box models\u2014is detrimental in high-stake scenarios, such as biomedical research, where patients and clinicians need to understand the causal relationships underpinning model predictions. The alternative to black-box models, mechanistic models, allow to guide precise, testable predictions; however, it may be challenging to readily apply these models to high-throughput data.<\/p>\n\n\n\n<p>By bringing together experts working in the fields of mechanistic, statistical, and AI modelling, this workshop aims to investigate how information about the molecular mechanisms underlying biological functions and cellular processes can be extracted from high-throughput data.<\/p>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-cdd954b\" data-block-id=\"cdd954b\"><style>.stk-cdd954b .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\">Target Audience<\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<p>The target audience ranges from students, early and advanced researchers to professionals working in the fields of Immunology, Systems Biology, Computational Biology, Computer Science and Bioinformatics who apply or are interested in applying computational modelling techniques and machine learning approaches for studying cellular functions of the immune system.<\/p>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-c302eb0\" data-block-id=\"c302eb0\"><style>.stk-c302eb0 .stk-block-heading__bottom-line{height:1px !important;background-color:var(u002du002dpaletteColor2,#f7b032) !important;margin-top:12px !important}<\/style><h5 class=\"stk-block-heading__text\">Programme<\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<figure class=\"wp-block-table tabla-ntb is-style-regular\" style=\"font-size:15px\"><table><thead><tr><th class=\"has-text-align-left\" data-align=\"left\"> <strong>T<\/strong>IME<\/th><th class=\"has-text-align-left\" data-align=\"left\"><strong>CONTENT<\/strong><\/th><\/tr><\/thead><tbody><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">14:00 &#8211; 14:10<\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\">Welcome and introduction to the workshop<\/span><\/strong><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">Session 1 &#8211; Statistical models<\/span><\/strong><br><span style=\"color: #444444;\" class=\"stk-highlight\">Chair: Mar\u00eda Rodr\u00edguez Mart\u00ednez<\/span><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>14:10 &#8211; 14:40<\/strong><\/span><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\">Data-driven model for neo-antigen\/T-cell presentation and<br>recognition: the case of pancreatic cancer.<\/span><\/strong><br><span style=\"color: #444444;\" class=\"stk-highlight\">Remi Monasson (Ecole Normale Sup\u00e9rieure, France)<\/span><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">14:40 &#8211; 15:10<\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\"><strong>Statistical and machine-learning analysis of adaptive immune<br>specificity<\/strong><\/span><br><span style=\"color: #444444;\" class=\"stk-highlight\">Victor Greiff (University of Oslo, Norway)<\/span><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">Session 2 &#8211; High throughput data analysis methods<\/span><\/strong><br>Chair: Anna Niarakis<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">15:10 &#8211; 15:40<\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\">Inferring shared intratumor heterogeneity from bulk RNA-seq<br>data without matched single-cell reference. <\/span><\/strong><br><span style=\"color: #444444;\" class=\"stk-highlight\">Valentina Boeva (ETH, Switzerland)<\/span><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><\/td><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\"><strong>Break<\/strong><\/span><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">16:00 &#8211; 16:30<\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\">Insights from topological data analysis of COVID-19 single-cell<br>transcriptomic<\/span><\/strong>.<br><span style=\"color: #444444;\" class=\"stk-highlight\">Davide Cirillo (Barcelona Supercomputing Center, Spain)<\/span><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">Session 3 &#8211; Machine learning<\/span><\/strong><br><span style=\"color: #444444;\" class=\"stk-highlight\">Chair: Matteo Barberis<\/span><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">16:30 &#8211; 17:00<\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\">Tackling the complexity of (unseen) epitope-TCR predictions.<\/span><\/strong><br><span style=\"color: #444444;\" class=\"stk-highlight\">Pieter Meysman (University of Antwerp, Belgium)<\/span><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>17:00 &#8211; 17:20<\/strong><\/span><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\">Interpretable machine learning to unravel T cell receptor binding rules. <\/span><\/strong><br><span style=\"color: #444444;\" class=\"stk-highlight\">Anna Weber (IBM Research Europe, Switzerland)<\/span><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">Session 4 &#8211; Discussion<\/span><\/strong><br><span style=\"color: #444444;\" class=\"stk-highlight\">Chairs: Mar\u00eda Rodr\u00edguez Mart\u00ednez, Anna Niarakis, Matteo<br>Barberis<\/span><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>17:20 &#8211; 17:50<\/strong><\/span><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\">Round table discussion with speakers and participants.<\/span><\/strong> Discussion on key challenges and open questions ofimmunological modelling moderated by the three organizers.<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">17:50 &#8211; 18:00<\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Closing remarks<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Title Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning Workshop details Date: Sunday, September 18th Time: 14:00 to 18:00 CEST (Slot 26) Format: Face-to-face Room: TBD Organisers Mar\u00eda Rodr\u00edguez-Mart\u00ednez Anna Niarakis Matteo Barberis Topic Interpretable machine learning &amp; statistical modelling of the immune system in health and disease settings Abstract The immune system is highly complex, and its malfunctioning can result in a wide number of disorders. A correct understanding of its inner workings is crucial to design optimal immunotherapies, develop new vaccines, understand the molecular basis of autoimmune diseases, etc. However, immune-related diseases pose specific\u2026<\/p>\n","protected":false},"author":2,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"blocksy_meta":{"styles_descriptor":{"styles":{"desktop":"","tablet":"","mobile":""},"google_fonts":[],"version":4},"page_title_panel":"","has_hero_section":"default","7b9ded946d4680066060ddae606a4869":"","hero_section":"type-1","hero_elements":[{"id":"custom_title","enabled":true,"heading_tag":"h1","title":"Inicio"},{"id":"custom_description","enabled":true,"description_visibility":{"desktop":true,"tablet":true,"mobile":false}},{"id":"custom_meta","enabled":false,"meta_elements":[{"id":"author","enabled":true,"label":"Por","has_author_avatar":"yes","avatar_size":25},{"id":"post_date","enabled":true,"label":"El","date_format_source":"default","date_format":"M j, Y"},{"id":"updated_date","enabled":false,"label":"El","date_format_source":"default","date_format":"M j, 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0.04)"}},"boxed_content_spacing":{"desktop":{"top":"40px","bottom":"40px","left":"40px","right":"40px","linked":true},"tablet":{"top":"35px","bottom":"35px","left":"35px","right":"35px","linked":true},"mobile":{"top":"20px","bottom":"20px","left":"20px","right":"20px","linked":true}},"content_boxed_radius":{"top":"3px","bottom":"3px","left":"3px","right":"3px","linked":true},"c17a8304e5e66c0922c2d4f80257a4e2":"","disable_featured_image":"no","disable_share_box":"no","disable_header":"no","disable_footer":"no"},"featured_image_urls":{"full":"","thumbnail":"","medium":"","medium_large":"","large":"","1536x1536":"","2048x2048":""},"post_excerpt_stackable":"<p>Title Computational modelling of immunological mechanisms: From statistical approaches to interpretable machine learning Workshop details Date: Sunday, September 18thTime: 14:00 to 18:00 CEST (Slot 26)Format: Face-to-faceRoom: TBD Organisers Mar\u00eda Rodr\u00edguez-Mart\u00ednezAnna NiarakisMatteo Barberis Topic Interpretable machine learning &amp; statistical modelling of the immune system in health and disease settings Abstract The immune system is highly complex, and its malfunctioning can result in a wide number of disorders. A correct understanding of its inner workings is crucial to design optimal immunotherapies, develop new vaccines, understand the molecular basis of autoimmune diseases, etc. However, immune-related diseases pose specific challenges associated with the incomplete&hellip;<\/p>\n","category_list":"","author_info":{"name":"victorcuencaharo","url":"https:\/\/eccb2022.org\/author\/victorcuencaharo\/"},"comments_num":"0 comments","_links":{"self":[{"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/pages\/1871"}],"collection":[{"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/comments?post=1871"}],"version-history":[{"count":11,"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/pages\/1871\/revisions"}],"predecessor-version":[{"id":2480,"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/pages\/1871\/revisions\/2480"}],"wp:attachment":[{"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/media?parent=1871"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}