{"id":"W2134975289","doi":"10.1093/bib/bbu044","title":"Comprehensive overview and assessment of computational prediction of microRNA targets in animals","year":2014,"lang":"en","type":"review","venue":"Briefings in Bioinformatics","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Computational biology; microRNA; Computer science; Identification (biology); Benchmark (surveying); Computational model; Biology; Gene; Artificial intelligence; 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":[0.001618467,0.001183882,0.001268376,0.001710758,0.0002111378,0.0008806851,0.00143626,0.0009287338,0.001445005],"category_scores_gemma":[0.002666617,0.0005749322,0.0008210365,0.002636942,0.0004072065,0.001134509,0.0006936226,0.001197422,0.001530515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007127448,"about_ca_system_score_gemma":0.001390843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00162814,"about_ca_topic_score_gemma":0.00135169,"domain_scores_codex":[0.9994867,0.0001373023,0.00006020812,0.00009933989,0.0001898049,0.00002673095],"domain_scores_gemma":[0.9986778,0.0008661528,0.00006846485,0.00004095298,0.0003048711,0.00004168988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007329084,0.00008130223,0.0005965239,0.01180476,0.0002376731,0.0001130876,0.00006792085,0.01020529,0.003519585,0.007616087,0.0196468,0.9460377],"study_design_scores_gemma":[0.00004670577,0.000473087,0.002657167,0.005728949,0.0004797861,0.0009740286,0.00008747258,0.02017614,0.01154401,0.01469145,0.9429871,0.0001539605],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001894657,0.9580153,0.03320049,0.0007843073,0.0004076306,0.00005379747,0.0002619121,0.0002801097,0.005101884],"genre_scores_gemma":[0.006811788,0.9613357,0.02905144,0.0003408744,0.0002839304,0.0000848862,0.0005520415,0.00008738146,0.001451872],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.001710758,"threshold_uncertainty_score":0.008559346,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03379539377691057,"score_gpt":0.3290256830012989,"score_spread":0.2952302892243883,"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."}}