{"id":1801,"date":"2022-05-25T16:30:02","date_gmt":"2022-05-25T16:30:02","guid":{"rendered":"https:\/\/eccb2022.org\/?page_id=1801"},"modified":"2022-09-12T12:31:27","modified_gmt":"2022-09-12T12:31:27","slug":"ntb-w01","status":"publish","type":"page","link":"https:\/\/eccb2022.org\/ntb-w01\/","title":{"rendered":"NTB-W01"},"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-a111ea7\" data-block-id=\"a111ea7\"><h3 class=\"stk-block-heading__text\">Machine Learning good practices &#8211; DOME recommendations for better Machine Learning in Computational Biology<\/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: Monday, September 12<\/li><li>Time: 13:30 to 16:30 CEST<\/li><li>Format: Virtual<\/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>Jennifer Harrow<\/strong>, ELIXIR Hub (United Kingdom)<\/li><li><strong>Fotis E. Psomopoulos<\/strong>, CERTH (Greece)<\/li><li><strong>Silvio Tosatto<\/strong>, Universit\u00e0 degli Studi di Padova (Italy)<\/li><li><strong>Leyla Jael Garc\u00eda-Castro<\/strong>, ZB MED Information Centre for Life Sciences (Germany)<\/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>This workshop will focus on :<\/p>\n\n\n\n<ul><li>Standards for reporting Machine Learning approaches on Life Sciences, particularly the DOME recommendations<\/li><li>FAIRness for Machine Learning<\/li><li>Best practices around Machine Learning<\/li><\/ul>\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<div class=\"wp-block-stackable-text stk-block-text stk-block stk-662b4da\" data-block-id=\"662b4da\"><p class=\"stk-block-text__text\">Large amounts of biological data are continuously created, processed and transformed. Machine Learning (ML) algorithms have become one of the preferred approaches for data processing and understanding given their capacity to deal with large amounts of data. In Life Sciences, ML is used in a variety of fields with the potential of resulting in ground-breaking medical applications. However, there are some issues around reproducibility, transparency, explainability and so on that could, at least partially, be alleviated by consistently applying good practices reflected in good descriptions and documentation wrt the ML model. This workshop\/SIG deals with good practices, guidelines and metadata for FAIR, reproducible and transparent ML in Life Sciences. In this first edition, we will focus on the Data, Optimization, Model and Evaluation (DOME) recommendations for supervised ML in biology which aim at facilitating the assessment of the quality and reliability of reported models.<\/p><\/div>\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\">Invited speakers<\/h5><div class=\"stk-block-heading__bottom-line\"><\/div><\/div>\n\n\n\n<p><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\"><strong>Fotis Psomopoulos<\/strong><br><\/span>Principal Investigator at the&nbsp;Institute of Applied Biosciences, Centre for Research and Technology Hellas (CERTH).<\/p>\n\n\n\n<p><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\"><strong>Daniel S. Katz<\/strong><br><\/span>Chief Scientist at the National Center for Supercomputing Applications (NCSA), Research Associate Professor in Computer Science (CS), Research Associate Professor in Electrical and Computer Engineering (ECE), Research Associate Professor in the School of Information Sciences (iSchool), and Faculty Affiliate in Computational Science and Engineering (CSE) at the University of Illinois Urbana-Champaign.<\/p>\n\n\n\n<p><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\"><strong>Macha Nikolski<\/strong><br><\/span>Head of the&nbsp;Computation Biology and Bioinformatics Lab&nbsp;at the CNRS institute in Bordeaux.<\/p>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-8f3d158\" data-block-id=\"8f3d158\"><style>.stk-8f3d158 .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>Researchers and research software engineers working on Machine\/Deep Learning approaches<\/p>\n\n\n\n<div class=\"wp-block-stackable-heading stk-block-heading stk-block stk-c394ce2\" data-block-id=\"c394ce2\"><style>.stk-c394ce2 .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\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>13:30 &#8211; 13:40<\/strong><\/span><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Welcoming and introduction to the workshop<\/strong><br>Leyla Jael Castro<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>13:40 &#8211; 13:55<\/strong><\/span><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Standards for reporting ML in LS<\/strong><br>Fotis Psomopoulos<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>13:55 &#8211; 14:10<\/strong><\/span><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>FAIRness for ML<\/strong><br>Daniel S. Katz<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\"><strong>14:10<\/strong> &#8211; <strong><strong>14:15<\/strong><\/strong><\/span> <\/td><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: #444444;\" class=\"stk-highlight\">Break<\/span><\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>14:15 &#8211; 14:30<\/strong><\/span><\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Challenges for AI in life sciences<\/strong><br>Macha Nikolski<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>14:30 &#8211; 14:45<\/strong><\/span><\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Generalizability through Heterogeneity<\/strong><br>Purvesh Khatri<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>14:45<\/strong><\/span><\/span><\/strong> &#8211; <strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>15:00<\/strong><\/span><\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Invited talk<\/strong><br>Chas Nelson<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\"><strong><strong>15:00<\/strong><\/strong> &#8211; <strong><strong>15:05<\/strong><\/strong><\/span><\/td><td class=\"has-text-align-left\" data-align=\"left\">Break<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">15:05 &#8211; 15:15<\/span><\/strong><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Overview on challenges and opportunities (input gathered from participants)<\/strong><br>Leyla Jael Castro<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\">15:15 &#8211; 16:15<\/span><\/strong><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong>Panel discussion on challenges and opportunities<\/strong><br>Invited speakers and panelists<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><strong>16:15<\/strong><\/strong> &#8211;<span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\"> <strong><strong><strong><strong>16:20<\/strong><\/strong><\/strong><\/strong><\/span><\/td><td class=\"has-text-align-left\" data-align=\"left\">Break<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>16:20<\/strong><\/span><\/span><\/strong> &#8211; 16:30<\/strong><\/span><\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><strong>Wrap-up<\/strong><\/strong><br>Leyla Jael Castro<\/td><\/tr><tr><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><span style=\"color: var(--paletteColor4, #0081a7);\" class=\"stk-highlight\"><strong>16:30<\/strong><\/span><\/span><\/strong><\/td><td class=\"has-text-align-left\" data-align=\"left\"><strong><span style=\"color: var(--paletteColor5, #1a1a1a);\" class=\"stk-highlight\">Coffee break and finding the next workshop location<\/span><\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n","protected":false},"excerpt":{"rendered":"<p>Title Machine Learning good practices &#8211; DOME recommendations for better Machine Learning in Computational Biology Workshop details Date: Monday, September 12 Time: 13:30 to 16:30 CEST Format: Virtual Organisers Jennifer Harrow, ELIXIR Hub (United Kingdom) Fotis E. Psomopoulos, CERTH (Greece) Silvio Tosatto, Universit\u00e0 degli Studi di Padova (Italy) Leyla Jael Garc\u00eda-Castro, ZB MED Information Centre for Life Sciences (Germany) Topic This workshop will focus on : Standards for reporting Machine Learning approaches on Life Sciences, particularly the DOME recommendations FAIRness for Machine Learning Best practices around Machine Learning Abstract Large amounts of biological data are continuously created, processed and transformed.\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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Psomopoulos, CERTH (Greece)Silvio Tosatto, Universit\u00e0 degli Studi di Padova (Italy)Leyla Jael Garc\u00eda-Castro, ZB MED Information Centre for Life Sciences (Germany) Topic This workshop will focus on : Standards for reporting Machine Learning approaches on Life Sciences, particularly the DOME recommendationsFAIRness for Machine LearningBest practices around Machine Learning Abstract Large amounts of biological data are continuously created, processed and transformed. Machine Learning (ML) algorithms have become one&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\/1801"}],"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=1801"}],"version-history":[{"count":24,"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/pages\/1801\/revisions"}],"predecessor-version":[{"id":3141,"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/pages\/1801\/revisions\/3141"}],"wp:attachment":[{"href":"https:\/\/eccb2022.org\/wp-json\/wp\/v2\/media?parent=1801"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}