{"id":"W2798884003","doi":"10.1186/s12859-018-2142-1","title":"Realizing drug repositioning by adapting a recommendation system to handle the process","year":2018,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Türkiye Bilimsel ve Teknolojik Araştırma Kurumu","keywords":"Drug repositioning; Computer science; Process (computing); Machine learning; Risk analysis (engineering); Collaborative filtering; Data science; Drug discovery; Precision medicine; Drug; Data mining; Recommender system; Artificial intelligence; Bioinformatics; Medicine","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":[0.001459707,0.0001229614,0.0001191196,0.0000876026,0.0005685427,0.0005444394,0.0006127856,0.00003001205,0.00000302802],"category_scores_gemma":[0.0002014091,0.00009449423,0.00003991292,0.0006186601,0.00003252372,0.001040824,0.0002557372,0.00009333516,0.00008283347],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001204423,"about_ca_system_score_gemma":0.0001192817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000287759,"about_ca_topic_score_gemma":0.00001605983,"domain_scores_codex":[0.9986458,0.0001374822,0.000493465,0.0001750775,0.0003124959,0.0002357112],"domain_scores_gemma":[0.9986871,0.0003624743,0.0002779324,0.0003664059,0.0002295129,0.00007653437],"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":[0.0001039644,0.0001738555,0.001054179,0.001942014,0.000134092,0.00000341778,0.1418075,0.1720551,0.0003047461,0.2187234,0.08120384,0.3824939],"study_design_scores_gemma":[0.0001108475,0.00003673599,0.00008284693,0.0001455188,0.000005280731,0.00003546541,0.002696445,0.9931986,0.001006136,0.0003049281,0.002241192,0.0001359898],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006225237,0.000009834422,0.9821702,0.0008185531,0.0003061026,0.0002993073,0.000006406365,0.0002147929,0.009949524],"genre_scores_gemma":[0.2043493,5.022532e-7,0.7947003,0.000658747,0.0001424253,0.00003822268,0.00002132806,0.000009712725,0.00007946601],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8211436,"threshold_uncertainty_score":0.5250041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02224553199740555,"score_gpt":0.303949745818363,"score_spread":0.2817042138209575,"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."}}