Network-level enrichment provides a framework for biological interpretation of machine learning results.

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Bibliographic Details
Title: Network-level enrichment provides a framework for biological interpretation of machine learning results.
Authors: Li J; Department of Statistics and Data Science, Washington University in St. Louis, MO, USA., Segel A; Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA., Feng X; Department of Statistics and Data Science, Washington University in St. Louis, MO, USA., Tu JC; Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA., Eck A; Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA., King KT; Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA., Adeyemo B; Department of Neurology, Washington University in St. Louis, MO, USA., Karcher NR; Department of Psychiatry, Washington University in St. Louis, MO, USA., Chen L; Department of Statistics and Data Science, Washington University in St. Louis, MO, USA., Eggebrecht AT; Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA., Wheelock MD; Mallinckrodt Institute of Radiology, Washington University in St. Louis, MO, USA.
Source: Network neuroscience (Cambridge, Mass.) [Netw Neurosci] 2024 Oct 01; Vol. 8 (3), pp. 762-790. Date of Electronic Publication: 2024 Oct 01 (Print Publication: 2024).
Publication Type: Journal Article
Journal Info: Publisher: MIT Press Country of Publication: United States NLM ID: 101719149 Publication Model: eCollection Cited Medium: Internet ISSN: 2472-1751 (Electronic) Linking ISSN: 24721751 NLM ISO Abbreviation: Netw Neurosci Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2472-1751
DOI:10.1162/netn_a_00383