{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Quinn TP"],"funding":["NIA NIH HHS","NIMH NIH HHS","NHGRI NIH HHS","NINDS NIH HHS","NIAMS NIH HHS"],"pagination":["387-401"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11228968"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["29(2)"],"pubmed_abstract":["Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in"],"journal":["Molecular psychiatry"],"pubmed_title":["A primer on the use of machine learning to distil knowledge from data in biological psychiatry."],"pmcid":["PMC11228968"],"funding_grant_id":["R01 HG011407","R21 MH126494","R01 AG064955","U01 AR076092","R01 NS128535","R01 MH116037"],"pubmed_authors":["Men X","Cao H","Schwarz E","Breen MS","Hauschild AC","Glatt SJ","Faraone SV","Zhang-James Y","Ahsen ME","Bichindaritz I","Barnett MM","Trakadis YJ","Meyer-Lindenberg A","Barnett EJ","Lin PI","Pandey G","Chen J","Cairns MJ","Hess JL","Elsheikh SSM","Nicodemus KK","Quinn TP","Hou J","Machine Learning in Psychiatry (MLPsych) Consortium","Marshe VS","Salekin A","Maciukiewicz M","Muller DJ"],"additional_accession":[]},"is_claimable":false,"name":"A primer on the use of machine learning to distil knowledge from data in biological psychiatry.","description":"Applications of machine learning in the biomedical sciences are growing rapidly. This growth has been spurred by diverse cross-institutional and interdisciplinary collaborations, public availability of large datasets, an increase in the accessibility of analytic routines, and the availability of powerful computing resources. With this increased access and exposure to machine learning comes a responsibility for education and a deeper understanding of its bases and bounds, borne equally by data scientists seeking to ply their analytic wares in medical research and by biomedical scientists seeking to harness such methods to glean knowledge from data. This article provides an accessible and critical review of machine learning for a biomedically informed audience, as well as its applications in","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Feb","modification":"2025-04-21T23:50:37.783Z","creation":"2025-04-05T19:19:15.632Z"},"accession":"S-EPMC11228968","cross_references":{"pubmed":["38177352"],"doi":["10.1038/s41380-023-02334-2"]}}