{"id":"W1925517254","doi":"10.1016/j.jbi.2015.06.027","title":"PhenoPredict: A disease phenome-wide drug repositioning approach towards schizophrenia drug discovery","year":2015,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"Intertek (Canada)","funders":"National Center for Advancing Translational Sciences; National Center for Chronic Disease Prevention and Health Promotion; National Center for Research Resources; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Cancer Institute","keywords":"Phenome; Drug repositioning; Drug; Drug discovery; Disease; Medicine; Drug development; Schizophrenia (object-oriented programming); Computational biology; Bioinformatics; Pharmacology; Psychiatry; Biology; Phenotype; Internal medicine; Genetics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003171415,0.0002646669,0.0004838018,0.0005129499,0.000144522,0.000744304,0.00155872,0.00006564421,0.000005050485],"category_scores_gemma":[0.001326219,0.0002045936,0.0002631242,0.0009341878,0.00024729,0.004742259,0.0006588003,0.0005793092,0.00002276391],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002901364,"about_ca_system_score_gemma":0.001956082,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008213116,"about_ca_topic_score_gemma":2.267276e-7,"domain_scores_codex":[0.995086,0.0002463868,0.001784388,0.0001709277,0.002312492,0.0003998575],"domain_scores_gemma":[0.9961949,0.000425387,0.001148091,0.0004973523,0.0005028306,0.001231441],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002091309,0.004773304,0.002335825,0.001802993,0.001394614,0.001145094,0.1040011,0.2306662,0.00006019002,0.08667485,0.3083886,0.2566659],"study_design_scores_gemma":[0.003740747,0.0002145668,0.002291042,0.0003632739,0.0001052655,0.0005093886,0.002044039,0.942679,0.00006319609,0.03450087,0.01295392,0.0005346852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0898975,0.0003669888,0.9039333,0.002515949,0.001284363,0.0001401045,0.00002072764,0.00006691503,0.00177408],"genre_scores_gemma":[0.3920419,0.00002857809,0.6056891,0.001275099,0.0007281564,0.000006674909,0.00004004237,0.00001910889,0.0001713751],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7120128,"threshold_uncertainty_score":0.8343084,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02278794271840391,"score_gpt":0.2731753927790659,"score_spread":0.250387450060662,"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."}}