{"id":"W2945833576","doi":"10.1145/3318299.3318323","title":"Predicting Drug-Drug Interactions Using Deep Neural Network","year":2019,"lang":"en","type":"article","venue":"","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; University of Manitoba","keywords":"DrugBank; Drug repositioning; Drug; Artificial neural network; Computer science; Artificial intelligence; Machine learning; Drug discovery; Test set; Receiver operating characteristic; Medicine; Pharmacology; Bioinformatics; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008278586,0.000869268,0.0007948137,0.001055635,0.00020518,0.0006168581,0.0004907184,0.0007137831,0.001068938],"category_scores_gemma":[0.002354139,0.0003454746,0.0006522702,0.0008918318,0.0002144753,0.0006326591,0.000484795,0.000942453,0.0002276477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009442118,"about_ca_system_score_gemma":0.001149031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01121367,"about_ca_topic_score_gemma":0.01362421,"domain_scores_codex":[0.9996044,0.0001223297,0.00003489632,0.00008219109,0.00009931863,0.00005694715],"domain_scores_gemma":[0.9990515,0.0006348828,0.0001335487,0.00003722566,0.0001080936,0.00003471356],"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":[0.0003380819,0.000330583,0.01749918,0.0001718644,0.0002712969,0.0002454331,0.00001963812,0.8844159,0.003513674,0.0009683504,0.002505739,0.08972025],"study_design_scores_gemma":[0.000006785568,0.00002696287,0.0005563762,0.000003300645,0.00001301825,0.00001373376,0.000002102171,0.9981654,0.0004630687,0.0005562163,0.0001904446,0.000002666797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6837457,0.00754255,0.2941216,0.002241258,0.0002063847,0.0001964225,0.004264086,0.002328492,0.005353388],"genre_scores_gemma":[0.9584227,0.001187088,0.03615562,0.0003151472,0.00004903347,0.00008358577,0.002352795,0.00002642868,0.001407661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01121367,"threshold_uncertainty_score":0.02229679,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02473160077719351,"score_gpt":0.3128649888920017,"score_spread":0.2881333881148082,"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."}}