{"id":"W1582076997","doi":"10.1007/11732990_11","title":"Detecting MicroRNA Targets by Linking Sequence, MicroRNA and Gene Expression Data","year":2006,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Microsoft","keywords":"microRNA; Computational biology; Gene; Gene expression; Biology; Regulation of gene expression; Genetics; Computer science","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":[0.001145118,0.0008167739,0.0009642274,0.002656524,0.0002315215,0.001123841,0.0005999714,0.0009030633,0.001449911],"category_scores_gemma":[0.002593307,0.0005164209,0.0008965501,0.002252028,0.0002965084,0.0009374093,0.000700471,0.0005586481,0.00238749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002107426,"about_ca_system_score_gemma":0.0003031034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004076479,"about_ca_topic_score_gemma":0.0009480336,"domain_scores_codex":[0.9993081,0.0001281964,0.00005536689,0.0002002369,0.0002552445,0.00005273385],"domain_scores_gemma":[0.9988796,0.0007229361,0.0001386587,0.0000894242,0.0001309229,0.00003852926],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007705768,0.0003269326,0.02546877,0.0007055036,0.0002684234,0.0004758343,0.0001223641,0.01249891,0.680334,0.0012242,0.002319825,0.2754847],"study_design_scores_gemma":[0.000108022,0.0007600253,0.03781418,0.0001071892,0.0004964059,0.002527409,0.0002500613,0.3022687,0.632543,0.01085403,0.01213523,0.0001357175],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3500252,0.002529394,0.6248307,0.0003113359,0.0001318234,0.0003391501,0.007228957,0.009090702,0.005512701],"genre_scores_gemma":[0.3719829,0.001479797,0.6068122,0.0002781875,0.0001137346,0.0004734654,0.01516829,0.0004734135,0.003218011],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002656524,"threshold_uncertainty_score":0.00605607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01682648169897041,"score_gpt":0.2507421171134429,"score_spread":0.2339156354144725,"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."}}