{"id":"W2897133367","doi":"10.1093/bioinformatics/bty878","title":"BMDExpress 2: enhanced transcriptomic dose-response analysis workflow","year":2018,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":227,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Canada","funders":"National Institute of Environmental Health Sciences; National Institutes of Health","keywords":"Workflow; Computer science; Software; Transcriptome; Software engineering; Data mining; Data science; Database; Operating system; Biology; Gene","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":[0.0002242649,0.0001434075,0.0001455129,0.0001511284,0.0001087406,0.00004589485,0.0002485839,0.0001429697,0.0001340848],"category_scores_gemma":[0.00005146385,0.0001277737,0.0001544415,0.0004269183,0.0001039137,0.000009499994,0.00004192216,0.00005231025,0.00009640239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001750604,"about_ca_system_score_gemma":0.00007618158,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002228154,"about_ca_topic_score_gemma":0.00001412961,"domain_scores_codex":[0.9990706,0.00005415192,0.0003095627,0.0001832038,0.0001627092,0.0002197242],"domain_scores_gemma":[0.9991131,0.000009244523,0.0001205124,0.0005377756,0.0001183465,0.0001009689],"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":[0.0007002221,0.00003514065,0.0001836549,0.00001188223,0.0001807349,1.507347e-7,0.0005861833,0.00007590882,0.978312,0.00002512407,0.006842582,0.01304646],"study_design_scores_gemma":[0.0006938564,0.0002448547,0.006666601,0.00001289923,0.0001420107,0.000001703891,0.0002091133,0.00384091,0.8834291,0.00003698003,0.104439,0.0002830936],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7500054,0.0001229453,0.2436288,0.0001585124,0.0003296876,0.0001854213,0.00002690385,0.00004127723,0.005501118],"genre_scores_gemma":[0.992379,0.00006213914,0.005286837,0.0004480881,0.0001885199,0.00002613656,0.00009485852,0.00001235597,0.001502101],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2423736,"threshold_uncertainty_score":0.5210459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0127195426686106,"score_gpt":0.2668319319701167,"score_spread":0.2541123893015061,"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."}}