{"id":"W2003575706","doi":"10.1371/journal.pone.0073168","title":"Identifying Cancer Specific Functionally Relevant miRNAs from Gene Expression and miRNA-to-Gene Networks Using Regularized Regression","year":2013,"lang":"en","type":"article","venue":"PLoS ONE","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; SickKids Foundation","funders":"","keywords":"microRNA; Computational biology; Interpretability; Context (archaeology); Biology; Gene expression; Bioinformatics; Gene; Computer science; Genetics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001345204,0.0009971762,0.0006931528,0.0009261893,0.0002665643,0.0006900431,0.0007824901,0.000699004,0.001538501],"category_scores_gemma":[0.003241012,0.000545489,0.001167485,0.0007060327,0.0004969552,0.0005502939,0.0005280476,0.001019656,0.0006162511],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007048687,"about_ca_system_score_gemma":0.0007511328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003622679,"about_ca_topic_score_gemma":0.004100989,"domain_scores_codex":[0.9995478,0.0001820232,0.00001460718,0.0001651925,0.00005616833,0.00003425534],"domain_scores_gemma":[0.9988112,0.0008810984,0.0001349161,0.0000644547,0.00008368268,0.00002466071],"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":[0.0001355866,0.00005821623,0.00345293,0.0001205276,0.0001236386,0.0001081266,0.000042992,0.9428311,0.01065407,0.003111072,0.001185504,0.03817623],"study_design_scores_gemma":[0.000003671509,0.000006456254,0.0003202384,0.000002281922,0.000006193052,0.00001213445,0.000003274724,0.9965928,0.001034137,0.001836858,0.000178689,0.000003177254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04649957,0.0002199484,0.9503977,0.0002429187,0.00001697857,0.00006185616,0.0005516832,0.001369896,0.0006394638],"genre_scores_gemma":[0.5542883,0.0003952385,0.4375657,0.0001843071,0.00004757514,0.0004950161,0.002421785,0.0004595598,0.004142669],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003622679,"threshold_uncertainty_score":0.007203162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04218743892015977,"score_gpt":0.2476778533629881,"score_spread":0.2054904144428283,"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."}}