{"id":11468,"date":"2025-08-26T09:40:28","date_gmt":"2025-08-26T00:40:28","guid":{"rendered":"https:\/\/www.ibs.re.kr\/bimag\/?post_type=tribe_events&p=11468"},"modified":"2025-08-26T09:40:28","modified_gmt":"2025-08-26T00:40:28","slug":"dynamical-data-science-and-ai-for-biology-and-medicine-luonan-chen","status":"publish","type":"tribe_events","link":"https:\/\/www.ibs.re.kr\/bimag\/event\/dynamical-data-science-and-ai-for-biology-and-medicine-luonan-chen\/","title":{"rendered":"Dynamical data science and AI for Biology and Medicine – Luonan Chen"},"content":{"rendered":"

Abstract<\/strong><\/p>\n

I will present a talk on “Dynamical data science and AI” for quantifying dynamical biological processes, disease progressions and various phenotypes, including dynamic network biomarkers (DNB) for early-warning signals of critical transitions, spatial-temporal information (STI) transformation for short-term time-series prediction, knockoff conditional mutual information (KOCMI) for quantifying interventional causality, partial cross-mapping (PCM) for causal inference among variables, and further AI applications to medicine. These methods are all data-driven or model-free approaches but based on the theoretical frameworks of nonlinear dynamics. We show the principles and advantages of dynamical data-science approaches for phenotype quantification as explicable, quantifiable, and generalizable. In particular, different from statistical data-science, dynamical data-science approaches exploit the essential features of dynamical systems in terms of data, e.g. strong fluctuations near a bifurcation point, low-dimensionality of a center manifold or an attractor, and phase-space reconstruction from a single variable by delay embedding theorem, and thus are able to provide different or additional information to the traditional approaches, i.e. statistics-based data science approaches. The dynamical data-science approaches for the quantifications of various phenotypes will further play an important role in the systematical research of various fields in biology and AI.<\/p>\n","protected":false},"excerpt":{"rendered":"

Abstract I will present a talk on “Dynamical data science and AI” for quantifying dynamical biological processes, disease progressions and various phenotypes, including dynamic network biomarkers (DNB) for early-warning signals … <\/p>\n

Continue reading “Dynamical data science and AI for Biology and Medicine – Luonan Chen”<\/span><\/a><\/p>\n","protected":false},"author":5,"featured_media":11469,"template":"","meta":{"_editorskit_title_hidden":false,"_editorskit_reading_time":0,"_editorskit_is_block_options_detached":false,"_editorskit_block_options_position":"{}","_uag_custom_page_level_css":"","_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[221],"class_list":["post-11468","tribe_events","type-tribe_events","status-publish","has-post-thumbnail","hentry","tribe_events_cat-biomedical-mathematics-colloquium","cat_biomedical-mathematics-colloquium"],"yoast_head":"\nDynamical data science and AI for Biology and Medicine - Luonan Chen - Biomedical Mathematics Group<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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