{"id":"W4402199095","doi":"10.1016/j.csbj.2024.08.027","title":"Identification of genetic basis of brain imaging by group sparse multi-task learning leveraging summary statistics","year":2024,"lang":"en","type":"article","venue":"Computational and Structural Biotechnology Journal","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; H. Lundbeck A/S; Servier; Eisai; Genentech; IXICO; National Natural Science Foundation of China; Northern California Institute for Research and Education; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; BioClinica; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Imaging genetics; Univariate; Genome-wide association study; Neuroimaging; Multivariate statistics; Computer science; Artificial intelligence; Identification (biology); Machine learning; Biology; Genetics; Neuroscience","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001960323,0.0008349324,0.000991215,0.0006331698,0.0002185692,0.0005818531,0.001180517,0.0008228214,0.001014987],"category_scores_gemma":[0.006145715,0.0003594374,0.001099611,0.0007232777,0.0005495551,0.0007957352,0.001045415,0.001262541,0.0003916324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002973464,"about_ca_system_score_gemma":0.0008178311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00202329,"about_ca_topic_score_gemma":0.002797925,"domain_scores_codex":[0.9994186,0.0002393742,0.00003359632,0.0001519412,0.0001023771,0.00005415301],"domain_scores_gemma":[0.9980004,0.001200701,0.0002815202,0.0002185051,0.00022553,0.00007326022],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003623129,0.0002329358,0.009688046,0.0002565361,0.0004193686,0.00030056,0.0001857201,0.6360806,0.02011234,0.01837724,0.005199766,0.3087845],"study_design_scores_gemma":[0.0000170174,0.00004927544,0.0009092979,0.000005852354,0.00002265506,0.00004828243,0.000008361966,0.9884283,0.001153615,0.008899337,0.0004462907,0.00001166355],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01969226,0.0001914989,0.9788929,0.0002519545,0.00001675618,0.00002727961,0.0001851754,0.0004480234,0.0002941447],"genre_scores_gemma":[0.571438,0.0004916191,0.4225428,0.000567974,0.0001855423,0.0002163825,0.002176644,0.0002285285,0.002152422],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00202329,"threshold_uncertainty_score":0.01036727,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007426593325077495,"score_gpt":0.2529643609327422,"score_spread":0.2455377676076647,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}