{"id":"W3041236926","doi":"10.1093/bib/bbaa133","title":"Predicting microRNA–disease associations from lncRNA–microRNA interactions via Multiview Multitask Learning","year":2020,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Cancer-related molecular mechanisms research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China","keywords":"microRNA; Computer science; Human multitasking; Machine learning; Artificial intelligence; Computational biology; Set (abstract data type); Similarity (geometry); Representation (politics); 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":[0.002811317,0.0009133644,0.0009992103,0.0009891338,0.0003307094,0.0008398624,0.0009310503,0.0009742039,0.0008492254],"category_scores_gemma":[0.004570956,0.000270473,0.000999824,0.0005996778,0.0004950011,0.0008776243,0.00117644,0.0009751655,0.000422577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005358487,"about_ca_system_score_gemma":0.0006625225,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002438522,"about_ca_topic_score_gemma":0.002285573,"domain_scores_codex":[0.9991154,0.0002918889,0.0000566172,0.0003097277,0.0001279255,0.00009847798],"domain_scores_gemma":[0.9971706,0.001699158,0.0003902178,0.0001888824,0.0003454064,0.0002055997],"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":[0.001127175,0.0008966801,0.06738972,0.0002118951,0.0005437966,0.0002907737,0.00014902,0.665503,0.01841334,0.001373858,0.002848884,0.2412519],"study_design_scores_gemma":[0.000005709463,0.00005697424,0.002241137,0.000003607413,0.00001609305,0.00003048097,0.000008915144,0.9952609,0.001468426,0.0008136463,0.00008710897,0.000007043755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4436038,0.001199688,0.5516314,0.0005805289,0.00006763557,0.00006619107,0.0005827846,0.001316595,0.0009514501],"genre_scores_gemma":[0.9560828,0.000114333,0.04252214,0.0001076801,0.00005068117,0.0000436959,0.0005352474,0.00002236541,0.0005210174],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002811317,"threshold_uncertainty_score":0.01486784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01649455683805146,"score_gpt":0.270659427394327,"score_spread":0.2541648705562756,"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."}}