{"id":"W3216512054","doi":"10.2174/1574893616666211119093100","title":"Machine Learning and Deep Learning Strategies in Drug Repositioning","year":2021,"lang":"en","type":"article","venue":"Current Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"National Natural Science Foundation of China","keywords":"Drug repositioning; Computer science; Drug; Drug discovery; Machine learning; Artificial intelligence; Preprocessor; Drug target; Data pre-processing; Data science; Medicine; Bioinformatics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006386323,0.0001244037,0.0001713306,0.0001436921,0.0001743998,0.0005930786,0.0001759834,0.00002564597,0.000006645562],"category_scores_gemma":[0.0002925803,0.0001288613,0.00004081737,0.00046447,0.00003168621,0.001293755,0.0003675631,0.0004268187,0.00001131982],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000046057,"about_ca_system_score_gemma":0.0001287392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007698104,"about_ca_topic_score_gemma":0.00001284525,"domain_scores_codex":[0.9987203,0.0002233881,0.0004296435,0.0001766789,0.0002408844,0.0002091067],"domain_scores_gemma":[0.9991799,0.0003768447,0.0001618284,0.0001347758,0.00008464967,0.00006199747],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000452111,0.0000772157,0.01138504,0.0004035545,0.00001821805,0.00005044564,0.01587735,0.2460014,0.00005572914,0.1346465,0.0000256811,0.5914543],"study_design_scores_gemma":[0.0002098193,0.00001367016,0.002637845,0.0001141037,0.000003481697,0.00007907951,0.001021985,0.9877313,0.0001267145,0.004069996,0.00383366,0.0001583713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06127087,0.003594412,0.9304572,0.0001626396,0.0003482703,0.0000761346,8.667834e-7,0.0001266047,0.003962981],"genre_scores_gemma":[0.6190507,0.0006622091,0.3800013,0.00004971514,0.00004745046,0.00000923529,0.00006637901,0.0000113196,0.0001017152],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7417299,"threshold_uncertainty_score":0.5719071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01732519701801303,"score_gpt":0.3018280753820009,"score_spread":0.2845028783639879,"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."}}