{"id":"W2133004689","doi":"10.1186/1748-7188-3-16","title":"HuMiTar: A sequence-based method for prediction of human microRNA targets","year":2008,"lang":"en","type":"article","venue":"Algorithms for Molecular Biology","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs; National Natural Science Foundation of China","keywords":"Computational biology; False positive paradox; Untranslated region; microRNA; Biology; Computer science; Base pair; Sequence (biology); Gene; Pairing; Function (biology); Messenger RNA; Genetics; Data mining; Bioinformatics; Artificial intelligence; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00165556,0.001510648,0.001026759,0.002608147,0.0005270342,0.0008564893,0.001473905,0.001271013,0.005326834],"category_scores_gemma":[0.003368692,0.0004767494,0.001268341,0.001104305,0.0004308418,0.0005885583,0.0007537672,0.0008595229,0.001744101],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006236899,"about_ca_system_score_gemma":0.001049072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002451888,"about_ca_topic_score_gemma":0.003696411,"domain_scores_codex":[0.9991289,0.0003003047,0.00005567802,0.00021804,0.0002386876,0.00005851769],"domain_scores_gemma":[0.9988744,0.0007760796,0.0001108011,0.00007255822,0.0001254142,0.00004068331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001326754,0.0002710645,0.009493862,0.0006417898,0.0005588623,0.0006599455,0.0001708231,0.3192081,0.02990867,0.006470635,0.02130452,0.6099851],"study_design_scores_gemma":[0.00004343333,0.00007007849,0.0008237208,0.00001811558,0.00003518848,0.0001660151,0.00001852517,0.9875993,0.005951607,0.002163883,0.003087172,0.00002298325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0369081,0.0008513545,0.9292914,0.0001973908,0.0000804724,0.0002562185,0.002009046,0.02804948,0.002356616],"genre_scores_gemma":[0.1671552,0.0002837701,0.8253583,0.0002725263,0.00004540169,0.000580008,0.003073141,0.001143531,0.002088152],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005326834,"threshold_uncertainty_score":0.01782006,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03353231989561144,"score_gpt":0.3287654067019258,"score_spread":0.2952330868063144,"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."}}