{"id":"W3026234875","doi":"10.1177/1176934320919707","title":"Deep belief network–Based Matrix Factorization Model for MicroRNA-Disease Associations Prediction","year":2020,"lang":"en","type":"article","venue":"Evolutionary Bioinformatics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Non-negative matrix factorization; Deep belief network; Classifier (UML); Artificial intelligence; microRNA; Computer science; Disease; Matrix decomposition; Deep learning; Machine learning; Feature (linguistics); Feature learning; Computational biology; Pattern recognition (psychology); Biology; Gene; Genetics; 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":[0.0001177618,0.0001821943,0.0001263333,0.00004165797,0.0002614053,0.00003391539,0.0001502905,0.0001677719,0.00001207267],"category_scores_gemma":[0.0003051942,0.0002057255,0.0001569762,0.0001656562,0.00003875054,0.00003670372,0.0000577881,0.00006448273,0.00001663481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009459344,"about_ca_system_score_gemma":0.0002796389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":6.495207e-7,"about_ca_topic_score_gemma":0.000001625251,"domain_scores_codex":[0.9988288,0.00003380901,0.0004372195,0.0002151299,0.0002131214,0.0002719131],"domain_scores_gemma":[0.9990101,0.00002276888,0.0002489936,0.0002313169,0.0002528951,0.0002338987],"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":[0.0001521006,0.00006533782,0.003454671,0.0001232423,0.00005359294,9.983995e-8,0.00009014584,0.9578294,0.006964738,0.0005541479,0.03047332,0.0002391707],"study_design_scores_gemma":[0.0007130967,0.00008979625,0.008417654,0.00001163741,0.00007649988,4.773041e-7,0.00001494543,0.9832385,0.0003215077,0.000472687,0.00644002,0.0002031362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00497908,0.0003750629,0.9916793,0.0006951177,0.0001574027,0.0006765516,0.001242969,0.0000839889,0.000110517],"genre_scores_gemma":[0.8405505,0.00004257128,0.13426,0.001230687,0.001019045,0.0001203982,0.02256249,0.00005019487,0.0001640611],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8574193,"threshold_uncertainty_score":0.8389242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01460747727715917,"score_gpt":0.2402476080074337,"score_spread":0.2256401307302745,"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."}}