multiVIB: A unified probabilistic contrastive learning framework for atlas-scale integration of single-cell multi-omics data.

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Bibliographic Details
Title: multiVIB: A unified probabilistic contrastive learning framework for atlas-scale integration of single-cell multi-omics data.
Authors: Xu Y; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA., Fleming SJ; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA., Wang B; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA., Schoenbeck EG; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA., Babadi M; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA., Huo BX; Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA.
Source: BioRxiv : the preprint server for biology [bioRxiv] 2025 Dec 01. Date of Electronic Publication: 2025 Dec 01.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2692-8205
DOI:10.1101/2025.11.29.691308