{"id":"W2940244905","doi":"10.1109/jbhi.2019.2911528","title":"Computational Identification of RNA-Seq Based miRNA-Mediated Prognostic Modules in Cancer","year":2019,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"National Natural Science Foundation of China","keywords":"Identification (biology); microRNA; Computer science; Computational biology; Cancer; RNA-Seq; RNA; Bioinformatics; Medicine; Transcriptome; Biology; Gene expression; Gene; Genetics; Internal 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001309798,0.0007280546,0.001191203,0.001193192,0.0004414292,0.0009906499,0.001051499,0.0007356545,0.002763808],"category_scores_gemma":[0.003592384,0.0005198105,0.001701772,0.0009361664,0.0003743115,0.0004470403,0.0006832064,0.0006150828,0.0004052057],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007810842,"about_ca_system_score_gemma":0.001322199,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003066847,"about_ca_topic_score_gemma":0.005197628,"domain_scores_codex":[0.999777,0.00007580852,0.00001431293,0.00007655253,0.0000307971,0.00002562448],"domain_scores_gemma":[0.9989041,0.0008405676,0.00007589135,0.00005321521,0.00007291124,0.00005338901],"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":[0.001496587,0.0002429415,0.0448905,0.0006328011,0.0007973299,0.0003689188,0.0001648613,0.8630963,0.01717399,0.00542408,0.004663449,0.06104813],"study_design_scores_gemma":[0.00003833518,0.00004791427,0.001498244,0.000008426055,0.00005073136,0.00002739203,0.00001274115,0.9925731,0.00190435,0.003379063,0.0004499998,0.000009660353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5044048,0.0007971345,0.4688705,0.0008611721,0.0001082252,0.0003556915,0.01164922,0.01037808,0.002575167],"genre_scores_gemma":[0.7480936,0.0003122194,0.2322763,0.0004169293,0.00005662615,0.0008213649,0.01620704,0.0004677029,0.001348262],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003066847,"threshold_uncertainty_score":0.009245872,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01645512374524805,"score_gpt":0.3062297619308842,"score_spread":0.2897746381856361,"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."}}