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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
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TZOFFSETFROM:+0900
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TZNAME:KST
DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250207T140000
DTEND;TZID=Asia/Seoul:20250207T160000
DTSTAMP:20260423T133751
CREATED:20250128T024238Z
LAST-MODIFIED:20250206T103822Z
UID:10708-1738936800-1738944000@www.ibs.re.kr
SUMMARY:A cell atlas foundation model for scalable search of similar human cells - Kevin Spinicci
DESCRIPTION:In this talk\, we discuss the paper “A cell atlas foundation model for scalable search of similar human cells” by Graham Heimberg et.al.\, Nature\, 2024 at the Journal Club. \nAbstract \n\n\nSingle-cell RNA sequencing has profiled hundreds of millions of human cells across organs\, diseases\, development and perturbations to date. Mining these growing atlases could reveal cell–disease associations\, identify cell states in unexpected tissue contexts and relate in vivo biology to in vitro models. These require a common measure of cell similarity across the body and an efficient way to search. Here we develop SCimilarity\, a metric-learning framework to learn a unified and interpretable representation that enables rapid queries of tens of millions of cell profiles from diverse studies for cells that are transcriptionally similar to an input cell profile or state. We use SCimilarity to query a 23.4-million-cell atlas of 412 single-cell RNA-sequencing studies for macrophage and fibroblast profiles from interstitial lung disease1 and reveal similar cell profiles across other fibrotic diseases and tissues. The top scoring in vitro hit for the macrophage query was a 3D hydrogel system2\, which we experimentally demonstrated reproduces this cell state. SCimilarity serves as a foundation model for single-cell profiles that enables researchers to query for similar cellular states across the human body\, providing a powerful tool for generating biological insights from the Human Cell Atlas.
URL:https://www.ibs.re.kr/bimag/event/scdiffusion-conditional-generation-of-high-quality-single-cell-data-using-diffusion-model-kevin-spinicci/
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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