{"id":"W4379768536","doi":"10.1186/s12859-023-05340-x","title":"Rescuing biologically relevant consensus regions across replicated samples","year":2023,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Université Laval","keywords":"Computer science; Bioconductor; Computational biology; Scripting language; R package; Data mining; Biology; Genetics; Gene","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":[0.01031559,0.002163099,0.002071405,0.002213269,0.002247327,0.001812184,0.002380036,0.002096294,0.00921159],"category_scores_gemma":[0.03017516,0.001270809,0.002688244,0.002132405,0.001795764,0.0007069238,0.002347228,0.002490493,0.006378606],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009524543,"about_ca_system_score_gemma":0.00261818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003756748,"about_ca_topic_score_gemma":0.00736181,"domain_scores_codex":[0.9915741,0.001427071,0.000696319,0.004388977,0.0014902,0.000423238],"domain_scores_gemma":[0.983834,0.006859293,0.001155061,0.005126945,0.002617833,0.0004068755],"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":[0.002440295,0.0003544368,0.04107763,0.006005215,0.001929651,0.001411294,0.002103823,0.01404415,0.7928982,0.005842916,0.01871835,0.113174],"study_design_scores_gemma":[0.0004122033,0.0005885294,0.08303157,0.0004255136,0.001492948,0.002077864,0.0007310358,0.1078134,0.7193094,0.01567123,0.0680999,0.00034637],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1819653,0.002221192,0.748873,0.0004788348,0.00118536,0.0007658533,0.0286232,0.03182168,0.004065578],"genre_scores_gemma":[0.2526132,0.0003968389,0.6995866,0.0007728132,0.000136033,0.002309356,0.03151827,0.01001735,0.002649523],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01031559,"threshold_uncertainty_score":0.05455464,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03774352314496041,"score_gpt":0.2834220550438373,"score_spread":0.2456785318988769,"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."}}