{"id":"W4225684933","doi":"10.1186/s12859-022-04647-5","title":"Supervised promoter recognition: a benchmark framework","year":2022,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Benchmark (surveying); Benchmarking; Machine learning; Deep learning; Computer science; Artificial intelligence; Promoter; DNA microarray; Computational biology; Process (computing); Data mining; Biology; Gene; Genetics","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":[0.004612034,0.001812086,0.0009971362,0.001752352,0.0006885155,0.001498312,0.004033623,0.00238687,0.002002069],"category_scores_gemma":[0.009323547,0.000401841,0.001297704,0.00192054,0.001120399,0.001775677,0.001550438,0.001823686,0.001223979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001897592,"about_ca_system_score_gemma":0.002170242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009114623,"about_ca_topic_score_gemma":0.008162782,"domain_scores_codex":[0.9971288,0.0008358092,0.0001866958,0.0009595242,0.0006733844,0.0002157714],"domain_scores_gemma":[0.9953055,0.001751355,0.0003640165,0.0009272285,0.001395717,0.0002561632],"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":[0.0007813274,0.0007232826,0.008226456,0.00109252,0.0002710138,0.0002863635,0.00008904647,0.7653422,0.01076453,0.01178598,0.02482179,0.1758156],"study_design_scores_gemma":[0.00005815054,0.0002959469,0.001368482,0.00005758461,0.00003398753,0.0001485466,0.00004496136,0.9643536,0.01525403,0.01224089,0.006112381,0.00003147502],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2580641,0.01009064,0.6702964,0.001968391,0.0005629401,0.0006743,0.02616487,0.01773223,0.01444604],"genre_scores_gemma":[0.5337206,0.002177604,0.369256,0.0005742721,0.0001962775,0.0009704359,0.08695082,0.0007618266,0.00539226],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009114623,"threshold_uncertainty_score":0.02439106,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01396104983278703,"score_gpt":0.2211159421678905,"score_spread":0.2071548923351035,"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."}}