{"id":"W2113390076","doi":"10.1038/nmeth1130","title":"Using expression profiling data to identify human microRNA targets","year":2007,"lang":"en","type":"article","venue":"Nature Methods","topic":"MicroRNA in disease regulation","field":"Biochemistry, Genetics and Molecular Biology","cited_by":399,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of New Brunswick; Ontario Institute for Cancer Research; University of Toronto","funders":"National Cancer Institute","keywords":"microRNA; Computational biology; Biology; Gene expression profiling; Gene expression; Bioinformatics; Gene; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001906763,0.0001556703,0.0001313423,0.00008730923,0.0001448289,0.00003207389,0.0004876977,0.0004463052,0.00002690529],"category_scores_gemma":[0.0004508111,0.0001506022,0.00005521209,0.0001546428,0.00002855807,0.000008981943,0.0005602455,0.0002524252,0.000003875316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003071198,"about_ca_system_score_gemma":0.00003985437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004193142,"about_ca_topic_score_gemma":0.000005173801,"domain_scores_codex":[0.9985671,0.0001951321,0.0002403306,0.0005680968,0.0001518888,0.0002774442],"domain_scores_gemma":[0.9986588,0.00002289985,0.0001034802,0.0009751554,0.000107296,0.0001323136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00004370599,0.00002390868,0.0004298006,0.00002244245,0.00001848485,0.000003742035,0.00001444618,0.00002415328,0.993955,0.00002080934,0.001518079,0.003925382],"study_design_scores_gemma":[0.0001667808,0.00001803536,0.005332049,0.00002931194,0.00001978987,0.000009556343,0.00001738268,0.00006542524,0.9601239,0.00009048345,0.03394828,0.0001790249],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5841026,0.00315352,0.4117616,0.00002510976,0.000393436,0.0002573746,0.00002967627,0.0000195442,0.0002571098],"genre_scores_gemma":[0.3685861,0.000003140024,0.6301589,0.0001983301,0.0004865482,0.000001915751,0.0004551384,0.00002659001,0.00008332812],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2183973,"threshold_uncertainty_score":0.6141379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08152713650975808,"score_gpt":0.4902602839792751,"score_spread":0.408733147469517,"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."}}