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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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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251001T160000
DTEND;TZID=Asia/Seoul:20251001T170000
DTSTAMP:20260501T120817
CREATED:20250826T003244Z
LAST-MODIFIED:20250922T073504Z
UID:11458-1759334400-1759338000@www.ibs.re.kr
SUMMARY:Topological Data Analysis for Multiscale Biology - Heather Harrington
DESCRIPTION:Abstract \nMany processes in the life sciences are inherently multi-scale and dynamic. Spatial structures and patterns vary across levels of organisation\, from molecular to multi-cellular to multi-organism. With more sophisticated mechanistic models and data available\, quantitative tools are needed to study their evolution in space and time. Topological data analysis (TDA) provides a multi-scale summary of data. I will review the main tools in topological data analysis and how single and multi-parameter persistent homology provide insights to biological systems.
URL:https://www.ibs.re.kr/bimag/event/tbd-heather-harrington/
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/Heather-Harrington.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251015T160000
DTEND;TZID=Asia/Seoul:20251015T170000
DTSTAMP:20260501T120817
CREATED:20250826T003811Z
LAST-MODIFIED:20250826T003811Z
UID:11464-1760544000-1760547600@www.ibs.re.kr
SUMMARY:Developing time-series machine learning methods to unlock new insights from large-scale biomedical resources - Aiden Doherty
DESCRIPTION:Abstract \nSmartphones and wearable devices provide a major opportunity to transform our understanding of the mechanisms\, determinants\, and consequences of diseases. For example\, around 9 in 10 people own a smartphone in the United Kingdom\, while one-fifth of US adults own wearable technologies. This high level of device ownership means that many people could contribute to health research from the comfort of their home by offering small amounts of time to share data and help address health-related questions that matter to them. A leading example is the seven day wrist-worn accelerometer data measured in 100\,000 UK Biobank participants between 2013-2015 that has led to important new findings. These include discoveries of: new genetic variants for sleep and activity; small amounts of vigorous non-exercise physical activity being associated with substantially lower mortality; and no apparent upper threshold to the benefits of physical activity with respect to cardiovascular disease risk. However\, challenges exist around cost\, access\, validity\, and training. In this talk I will review progress made in this exciting new area of health data science and share opportunities for self-supervised time-series machine learning to provide new insights into physical activity\, sleep\, heart rhythms and other exposures relevant to health and disease.
URL:https://www.ibs.re.kr/bimag/event/developing-time-series-machine-learning-methods-to-unlock-new-insights-from-large-scale-biomedical-resources-aiden-doherty/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/webp:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2025/08/Aiden-Doherty-e1756168683328.webp
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20251029T160000
DTEND;TZID=Asia/Seoul:20251029T170000
DTSTAMP:20260501T120817
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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