{"id":"W2558702637","doi":"10.1093/nar/gkw1085","title":"Prediction of human miRNA target genes using computationally reconstructed ancestral mammalian sequences","year":2016,"lang":"en","type":"article","venue":"Nucleic Acids Research","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal; McGill University","funders":"Fonds de recherche du Québec – Nature et technologies; Université du Québec à Montréal; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Biology; In silico; Computational biology; Gene; microRNA; Genome; Untranslated region; Genetics; Human genome; RNA","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007405212,0.0006159357,0.0005133643,0.0008308226,0.0004178858,0.0005936422,0.0005150093,0.0005657737,0.001999653],"category_scores_gemma":[0.002581843,0.0004216057,0.0008084856,0.0005629183,0.0002337241,0.000375772,0.0005136497,0.0004684618,0.0007757613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003770835,"about_ca_system_score_gemma":0.000748902,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001979505,"about_ca_topic_score_gemma":0.003490388,"domain_scores_codex":[0.9997496,0.00007069907,0.00001961311,0.00009472227,0.0000443313,0.0000209854],"domain_scores_gemma":[0.999483,0.000358825,0.00004065115,0.00003576799,0.00005822686,0.00002352436],"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":[0.003009605,0.0002692312,0.06377732,0.0005730052,0.0004814728,0.001905989,0.0003698239,0.4694018,0.09394276,0.006931922,0.008684451,0.3506525],"study_design_scores_gemma":[0.00006305774,0.0001002194,0.0036171,0.00001762848,0.00004653566,0.0003084975,0.00005148626,0.9787397,0.01309859,0.002044578,0.001896668,0.00001588423],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5581794,0.0007192694,0.4239469,0.0003391748,0.00004862522,0.0001083706,0.002639008,0.0108146,0.003204642],"genre_scores_gemma":[0.6643056,0.0003508979,0.3262466,0.0001216298,0.00002123536,0.0001030434,0.007096877,0.0005181326,0.001235924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001999653,"threshold_uncertainty_score":0.006689489,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0797073283988154,"score_gpt":0.3492189446947716,"score_spread":0.2695116162959562,"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."}}