{"id":"W3036062177","doi":"10.1093/bioinformatics/btaa577","title":"MDIPA: a microRNA–drug interaction prediction approach based on non-negative matrix factorization","year":2020,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; microRNA; Similarity (geometry); Computational biology; Matrix decomposition; Identification (biology); Data mining; Artificial intelligence; Machine learning; Biology; Gene; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008794282,0.001229469,0.001058331,0.001043753,0.0004102384,0.0005436774,0.001077774,0.0008854188,0.003938068],"category_scores_gemma":[0.002314992,0.0004011679,0.001186061,0.0006001533,0.0002654511,0.0005732559,0.0007968378,0.00115806,0.001218174],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004841307,"about_ca_system_score_gemma":0.001386943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004277004,"about_ca_topic_score_gemma":0.006066368,"domain_scores_codex":[0.9996356,0.0001034409,0.00002179771,0.0000874102,0.0001195961,0.00003205856],"domain_scores_gemma":[0.9993407,0.0003767819,0.0000857039,0.00003135353,0.0001146144,0.00005088715],"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":[0.0007381211,0.0004214394,0.006349645,0.001315177,0.000483774,0.0006834605,0.0001092198,0.4936773,0.03137994,0.007273984,0.04095602,0.4166119],"study_design_scores_gemma":[0.00004303488,0.00008410078,0.0003489324,0.00001229096,0.00002466993,0.0001188585,0.000008168178,0.9918894,0.002254215,0.002336526,0.002861985,0.00001780905],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01640275,0.001580133,0.9691688,0.0006558577,0.0001197444,0.0003246248,0.002483093,0.007331339,0.00193374],"genre_scores_gemma":[0.1990774,0.001136999,0.7906714,0.0004916326,0.0001528559,0.000648895,0.005245447,0.0003045223,0.002270904],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004277004,"threshold_uncertainty_score":0.01317418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0276237981087063,"score_gpt":0.283240223459039,"score_spread":0.2556164253503327,"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."}}