{"id":"W2899971763","doi":"10.1093/bioinformatics/bty911","title":"Discovering network phenotype between genetic risk factors and disease status via diagnosis-aligned multi-modality regression method in Alzheimer’s disease","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute on Aging; Canadian Institutes of Health Research; National Institutes of Health; Stanley Medical Research Institute; National Natural Science Foundation of China; U.S. Department of Defense","keywords":"Disease; Phenotype; Regression; Clinical phenotype; Computational biology; Computer science; Artificial intelligence; Medicine; Bioinformatics; Genetics; Biology; Statistics; Pathology; Gene; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003502505,0.0003270122,0.0003353209,0.0001115247,0.0005036873,0.0001005506,0.0001932204,0.00006397827,0.00001455274],"category_scores_gemma":[0.007752988,0.0002542941,0.00007959165,0.0003930528,0.0002934991,0.0005457678,0.0004120542,0.0001474827,0.00002494454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008509755,"about_ca_system_score_gemma":0.00009927358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002626312,"about_ca_topic_score_gemma":0.0001125764,"domain_scores_codex":[0.9978141,0.0002954274,0.0005051066,0.0004187631,0.0003977392,0.0005688353],"domain_scores_gemma":[0.9951664,0.00349136,0.0002960666,0.0004433762,0.00004741954,0.0005554388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001278962,0.00007918441,0.9860976,0.00006320944,0.00002919537,0.000005719162,0.001326275,0.00109456,0.00003754878,0.00006528847,0.0004344624,0.01063907],"study_design_scores_gemma":[0.0005319628,0.00008306024,0.9101578,0.00008548421,0.0001399002,3.135159e-7,0.0001114422,0.08563853,0.0006627942,0.00158136,0.0006717758,0.0003355518],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9418461,0.0005233071,0.05517321,0.0005732374,0.0005165385,0.0007448892,0.0004395465,0.0001253966,0.00005778704],"genre_scores_gemma":[0.9833213,0.0002493484,0.01567014,0.0004387462,0.0002184836,0.00005240364,0.00001617736,0.00002647992,0.000006914802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08454397,"threshold_uncertainty_score":0.9999909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06549029352753541,"score_gpt":0.3238470154703463,"score_spread":0.2583567219428109,"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."}}