Learning to estimate sample-specific transcriptional networks for 7,000 tumors.

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Bibliographic Details
Title: Learning to estimate sample-specific transcriptional networks for 7,000 tumors.
Authors: Ellington CN; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213., Lengerich BJ; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139.; Broad Institute, Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142., Watkins TBK; Cancer Institute, University College London, London WC1E 6DD, United Kingdom., Yang J; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139.; Broad Institute, Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142., Adduri AK; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213., Mahbub S; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213., Xiao H; Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260., Kellis M; Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139.; Broad Institute, Massachusetts Institute of Technology and Harvard University, Cambridge, MA 02142., Xing EP; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213.; Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence, Masdar City SE45 05, Abu Dhabi, United Arab Emirates.; GenBio AI Inc., Palo Alto, CA 94301.
Source: Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2025 May 27; Vol. 122 (21), pp. e2411930122. Date of Electronic Publication: 2025 May 23.
Publication Type: Journal Article
Journal Info: Publisher: National Academy of Sciences Country of Publication: United States NLM ID: 7505876 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1091-6490 (Electronic) Linking ISSN: 00278424 NLM ISO Abbreviation: Proc Natl Acad Sci U S A Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1091-6490
DOI:10.1073/pnas.2411930122