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PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
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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
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250718T140000
DTEND;TZID=Asia/Seoul:20250718T160000
DTSTAMP:20260423T031426
CREATED:20250701T022224Z
LAST-MODIFIED:20250701T022224Z
UID:11231-1752847200-1752854400@www.ibs.re.kr
SUMMARY:scGPT: toward building a foundation model for single-cell multi-omics using generative AI - Hyun Kim
DESCRIPTION:In this talk\, we discuss the paper “scGPT: toward building a foundation model for single-cell multi-omics using generative AI” by Haotian Cui\, et.al. Nature Methods\, 2024. \nAbstract \nGenerative pretrained models have achieved remarkable success in various domains such as language and computer vision. Specifically\, the combination of large-scale diverse datasets and pretrained transformers has emerged as a promising approach for developing foundation models. Drawing parallels between language and cellular biology (in which texts comprise words; similarly\, cells are defined by genes)\, our study probes the applicability of foundation models to advance cellular biology and genetic research. Using burgeoning single-cell sequencing data\, we have constructed a foundation model for single-cell biology\, scGPT\, based on a generative pretrained transformer across a repository of over 33 million cells. Our findings illustrate that scGPT effectively distills critical biological insights concerning genes and cells. Through further adaptation of transfer learning\, scGPT can be optimized to achieve superior performance across diverse downstream applications. This includes tasks such as cell type annotation\, multi-batch integration\, multi-omic integration\, perturbation response prediction and gene network inference.
URL:https://www.ibs.re.kr/bimag/event/scgpt-toward-building-a-foundation-model-for-single-cell-multi-omics-using-generative-ai-hyun-kim/
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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