{"id":"W4237555822","doi":"10.21203/rs.3.rs-56433/v1","title":"SNF-NN: Computational Method To Predict Drug-Disease Interactions Using Similarity Network Fusion and Neural Networks","year":2020,"lang":"en","type":"preprint","venue":"Research Square","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Robustness (evolution); Machine learning; Artificial intelligence; Drug repositioning; Computer science; Artificial neural network; Similarity (geometry); Drug discovery; Drug development; Drug; Cross-validation; Data mining; Bioinformatics; Medicine; Biology; 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":[0.002007956,0.0008083832,0.001514491,0.002226755,0.0006074564,0.0008391344,0.001707875,0.001578636,0.005059218],"category_scores_gemma":[0.005050815,0.0004547418,0.001212901,0.001988719,0.0004434274,0.001267564,0.001060357,0.001074909,0.001443631],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000641561,"about_ca_system_score_gemma":0.00148114,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006133919,"about_ca_topic_score_gemma":0.007784432,"domain_scores_codex":[0.999414,0.0001629823,0.00003853399,0.0001083635,0.0002311107,0.00004505411],"domain_scores_gemma":[0.9988856,0.0006164683,0.00007404363,0.0001279918,0.0002439195,0.00005203731],"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":[0.0005373618,0.0002348993,0.004140934,0.0003305745,0.0004671015,0.0001705115,0.00006425931,0.4966651,0.003380767,0.01337474,0.01589195,0.4647417],"study_design_scores_gemma":[0.00001764553,0.00001993239,0.0001565961,0.000004822149,0.00001649396,0.00002999206,0.000003161718,0.9945215,0.0005656051,0.004034291,0.0006251559,0.000004774868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02297451,0.0007233074,0.9676091,0.000251692,0.0001312728,0.0001691667,0.001109709,0.00540106,0.001630085],"genre_scores_gemma":[0.2440284,0.0004240572,0.7490069,0.0002022364,0.0001322567,0.0003850484,0.002301896,0.0003663412,0.003152854],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006133919,"threshold_uncertainty_score":0.01692474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1345548739533749,"score_gpt":0.469979856742913,"score_spread":0.3354249827895381,"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."}}