{"id":"W3043467106","doi":"10.1093/bioinformatics/btaa434","title":"Identifying diagnosis-specific genotype–phenotype associations via joint multitask sparse canonical correlation analysis and classification","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Functional Brain Connectivity Studies","field":"Neuroscience","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; H. Lundbeck A/S; Servier; National Natural Science Foundation of China; Eisai; China Postdoctoral Science Foundation; National Institutes of Health; Northwestern Polytechnical University; Genentech; IXICO; Northern California Institute for Research and Education; Novartis Pharmaceuticals Corporation; BioClinica; Biogen; Pfizer; Northwestern University; University of Pennsylvania; University of Southern California; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Canonical correlation; Correlation; Phenotype; Joint (building); Genotype; Computational biology; Computer science; Genetics; Artificial intelligence; Biology; Mathematics; Gene","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":[],"consensus_categories":[],"category_scores_codex":[0.0002148435,0.0001508803,0.0002496501,0.0001690328,0.000395803,0.0001387067,0.0001081759,0.00008839678,0.00004407738],"category_scores_gemma":[0.004379508,0.0001508335,0.00009285875,0.001126625,0.00009543266,0.0004844299,0.0001124701,0.0001876565,0.0002707978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001421106,"about_ca_system_score_gemma":0.00004048102,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002814713,"about_ca_topic_score_gemma":0.00008455167,"domain_scores_codex":[0.9985664,0.00007932397,0.0004794622,0.0002775736,0.0003950243,0.0002022239],"domain_scores_gemma":[0.9977915,0.001489395,0.0003097825,0.0001946772,0.00009475983,0.0001198771],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000293518,0.0009861639,0.5331125,0.0006104783,0.002074514,0.00003060747,0.05314538,0.01775087,0.1840331,0.06734616,0.05815762,0.08245905],"study_design_scores_gemma":[0.0004301498,0.00007599595,0.533424,0.000009881355,0.0003018779,0.000002425292,0.0004921731,0.4561872,0.00339832,0.000278374,0.005079636,0.0003199589],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3981617,0.0004084195,0.5737638,0.01816741,0.001422187,0.00163538,0.0005437977,0.000751055,0.005146252],"genre_scores_gemma":[0.9916893,0.0001826696,0.005949616,0.001945216,0.00009337172,0.00003264476,0.00006475304,0.00001258327,0.00002987817],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5935276,"threshold_uncertainty_score":0.6150813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1246065931131403,"score_gpt":0.282766982082125,"score_spread":0.1581603889689847,"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."}}