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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Seoul:20251029T160000
DTEND;TZID=Asia/Seoul:20251029T170000
DTSTAMP:20260501T135818
CREATED:20250826T004028Z
LAST-MODIFIED:20250826T004028Z
UID:11468-1761753600-1761757200@www.ibs.re.kr
SUMMARY:Dynamical data science and AI for Biology and Medicine - Luonan Chen
DESCRIPTION:Abstract \nI 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.
URL:https://www.ibs.re.kr/bimag/event/dynamical-data-science-and-ai-for-biology-and-medicine-luonan-chen/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/08/Luonan-Chen-e1756168815720.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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