{"id":"W2346950316","doi":"10.1093/bioinformatics/btw228","title":"Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm","year":2016,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":470,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Program for New Century Excellent Talents in University; U.S. Food and Drug Administration; National Natural Science Foundation of China; Stowers Institute for Medical Research","keywords":"Similarity (geometry); Drug repositioning; Drug; Computer science; Disease; Random walk; Machine learning; Drug development; Data mining; Artificial intelligence; Mathematics; Medicine; Statistics; Pharmacology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009382617,0.001060177,0.001972119,0.002943567,0.0005333252,0.0009480782,0.001732554,0.001370799,0.001643791],"category_scores_gemma":[0.002888524,0.000478305,0.001233205,0.001888738,0.0005628614,0.001558106,0.0009462071,0.0008045538,0.0003537487],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007669459,"about_ca_system_score_gemma":0.001526616,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006409603,"about_ca_topic_score_gemma":0.005367205,"domain_scores_codex":[0.9991711,0.0001876641,0.00009366151,0.0002197232,0.0002504481,0.00007739447],"domain_scores_gemma":[0.99886,0.0005776254,0.0001778875,0.0000915309,0.0001987851,0.00009417343],"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":[0.0001103524,0.0001680975,0.00320752,0.0001583078,0.0001457237,0.0001628368,0.00005396845,0.8350003,0.002484581,0.007546526,0.001779542,0.1491821],"study_design_scores_gemma":[0.000009408142,0.00002994204,0.0001443801,0.000003473412,0.00001239614,0.0000342973,0.000004214147,0.9976914,0.0002831649,0.001601521,0.0001807823,0.00000498867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0411038,0.0006829105,0.9551173,0.0002235298,0.00004837935,0.00017158,0.000140434,0.0008871612,0.001624979],"genre_scores_gemma":[0.5372532,0.0005936229,0.4578458,0.0002109184,0.00007406106,0.0004504354,0.0008731072,0.0001139107,0.00258492],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006409603,"threshold_uncertainty_score":0.01274461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02364108547326174,"score_gpt":0.2691134950196628,"score_spread":0.2454724095464011,"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."}}