{"id":"W2516607805","doi":"10.1142/s0218339016500157","title":"COMPUTATIONAL EVIDENCE FROM TWO CORRELATED DATA SOURCES AT DIFFERENT MOLECULAR LEVELS FOR AF-VHD-SPECIFIC MICRORNA SIGNATURE","year":2016,"lang":"en","type":"article","venue":"Journal of Biological Systems","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Science Foundation of Guangdong Province; National Natural Science Foundation of China; McMaster University; National Science Foundation","keywords":"microRNA; Computational biology; Mechanism (biology); Biology; Atrial fibrillation; Identification (biology); Disease; Microarray; Bioinformatics; Gene; Gene expression; Medicine; Genetics; Pathology; Cardiology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003048461,0.0006187208,0.0008983415,0.001880179,0.0006633947,0.001824306,0.0009726542,0.0007944778,0.002872366],"category_scores_gemma":[0.01767613,0.0005005479,0.001624014,0.001209134,0.000497099,0.001650815,0.0009106108,0.001023269,0.0002895763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008689595,"about_ca_system_score_gemma":0.001977816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005978474,"about_ca_topic_score_gemma":0.006771855,"domain_scores_codex":[0.9986773,0.0004121482,0.0001446427,0.0003557365,0.0002962235,0.0001138871],"domain_scores_gemma":[0.9863569,0.01177883,0.0004397221,0.0004256059,0.0008255765,0.0001733638],"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":[0.001431359,0.000416144,0.09538622,0.0003248275,0.000712578,0.0006340822,0.00009951146,0.8248297,0.003341141,0.005905462,0.001723565,0.06519543],"study_design_scores_gemma":[0.00001650626,0.00002104791,0.001848177,0.000006659067,0.00003858708,0.00003635278,0.000009233972,0.9963425,0.0004160485,0.001186369,0.00007344409,0.000005134091],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7586086,0.000536932,0.2316231,0.001579563,0.00009072274,0.0002269855,0.002304313,0.001240733,0.003788999],"genre_scores_gemma":[0.9434198,0.0001156837,0.05307781,0.0001551809,0.00002074959,0.0001171697,0.00259684,0.00004485606,0.0004521098],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005978474,"threshold_uncertainty_score":0.01612204,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06505037397407207,"score_gpt":0.2944329304042437,"score_spread":0.2293825564301717,"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."}}