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X-WR-CALNAME:Biomedical Mathematics Group
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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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TZNAME:KST
DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260109T100000
DTEND;TZID=Asia/Seoul:20260109T113000
DTSTAMP:20260422T161106
CREATED:20251231T002857Z
LAST-MODIFIED:20251231T002857Z
UID:12078-1767952800-1767958200@www.ibs.re.kr
SUMMARY:scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction - Aqsa Awan
DESCRIPTION:In this talk\, we discuss the paper “scPPDM: A Diffusion Model for Single-Cell Drug-Response Prediction” by Z. Liang et al.\, arxiv\, 2025. \nAbstract \nThis paper introduces the Single-Cell Perturbation Prediction Diffusion Model (scPPDM)\, the first diffusion-based framework for single-cell drug-response prediction from scRNA-seq data. scPPDM couples two condition channels\, pre-perturbation state and drug with dose\, in a unified latent space via non-concatenative GD-Attn. During inference\, factorized classifier-free guidance exposes two interpretable controls for state preservation and drug-response strength and maps dose to guidance magnitude for tunable intensity. Evaluated on the Tahoe-100M benchmark under two stringent regimes\, unseen covariate combinations (UC) and unseen drugs (UD)\, scPPDM sets new state-of-the-art results across log fold-change recovery\, delta correlations\, explained variance\, and DE-overlap. Representative gains include +36.11%/+34.21% on DEG logFC-Spearman/Pearson in UD over the second-best model. This control interface enables transparent what-if analyses and dose tuning\, reducing experimental burden while preserving biological specificity.
URL:https://www.ibs.re.kr/bimag/event/scppdm-a-diffusion-model-for-single-cell-drug-response-prediction-aqsa-awan/
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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