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X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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DTSTART:20240101T000000
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DTSTART;TZID=Asia/Seoul:20251212T110000
DTEND;TZID=Asia/Seoul:20251212T123000
DTSTAMP:20260422T195142
CREATED:20251026T141250Z
LAST-MODIFIED:20251215T043112Z
UID:11796-1765537200-1765542600@www.ibs.re.kr
SUMMARY:Quantifying interventional causality by knockoff operation - Yun Min Song
DESCRIPTION:In this talk\, we discuss the paper “Causal disentanglement for single-cell representations and controllable counterfactual generation” by Yicheng Gao et al.\, Nature Communications\, 2025. \nAbstract  \nConducting disentanglement learning on single-cell omics data offers a promising alternative to traditional black-box representation learning by separating the semantic concepts embedded in a biological process. We present CausCell\, which incorporates the factual information about causal relationships among disentangled concepts within a diffusion model to generate more reliable disentangled cellular representations\, with the aim of increasing the explainability\, generalizability and controllability of single-cell data\, including spatial-temporal omics data\, relative to those of the existing black-box representation learning models. Two quantitative evaluation scenarios\, i.e.\, disentanglement and reconstruction\, are presented to conduct the first comprehensive single-cell disentanglement learning benchmark\, which demonstrates that CausCell outperforms the state-of-the-art methods in both scenarios. Additionally\, CausCell can implement controllable generation by intervening with the concepts of single-cell data when given a causal structure. It also has the potential to uncover biological insights by generating counterfactuals from small and noisy single-cell datasets.
URL:https://www.ibs.re.kr/bimag/event/journal-club-yun-min-song/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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