BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data.

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Bibliographic Details
Title: BIDCell: Biologically-informed self-supervised learning for segmentation of subcellular spatial transcriptomics data.
Authors: Fu X; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.; School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia.; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW, 2006, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.; Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China., Lin Y; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW, 2006, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.; Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China., Lin DM; Department of Biomedical Sciences, Cornell University, Ithaca, NY, 14850, USA., Mechtersheimer D; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW, 2006, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia., Wang C; School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia.; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW, 2006, Australia.; Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China., Ameen F; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW, 2006, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia., Ghazanfar S; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW, 2006, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia., Patrick E; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW, 2006, Australia.; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.; Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China.; The Westmead Institute for Medical Research, Sydney, NSW, 2145, Australia., Kim J; School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia.; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW, 2006, Australia.; Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China., Yang JYH; School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia. jean.yang@sydney.edu.au.; Sydney Precision Data Science Centre, University of Sydney, Sydney, NSW, 2006, Australia. jean.yang@sydney.edu.au.; Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia. jean.yang@sydney.edu.au.; Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China. jean.yang@sydney.edu.au.
Source: Nature communications [Nat Commun] 2024 Jan 13; Vol. 15 (1), pp. 509. Date of Electronic Publication: 2024 Jan 13.
Publication Type: Journal Article; Research Support, Non-U.S. Gov't
Journal Info: Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE
Database: MEDLINE Ultimate
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ISSN:2041-1723
DOI:10.1038/s41467-023-44560-w