{"id":"W2030528974","doi":"10.1186/1471-2105-12-25","title":"Systematic error detection in experimental high-throughput screening","year":2011,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University and Génome Québec Innovation Centre; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Computer science; Context (archaeology); Error detection and correction; Data mining; Systematic error; Statistical power; Type I and type II errors; Process (computing); Statistics; Algorithm; Reliability engineering; Mathematics; Biology","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":[0.03907383,0.00170615,0.002131247,0.004132865,0.001047757,0.002327511,0.003678942,0.002394317,0.001553695],"category_scores_gemma":[0.1332721,0.0008444227,0.001973099,0.004034612,0.003252093,0.001755326,0.002847962,0.001908815,0.0007836586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001813896,"about_ca_system_score_gemma":0.002331951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001079134,"about_ca_topic_score_gemma":0.0009372284,"domain_scores_codex":[0.9367468,0.02982691,0.005256898,0.00572837,0.02124684,0.001194189],"domain_scores_gemma":[0.7652895,0.172606,0.02144021,0.02032047,0.0192713,0.001072485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.005726178,0.001380901,0.1800208,0.01240082,0.002772805,0.001955528,0.001980706,0.2205558,0.1502156,0.02557463,0.007267348,0.3901488],"study_design_scores_gemma":[0.0002971887,0.002616756,0.05763269,0.0007793093,0.0004823706,0.001818063,0.0003482144,0.5185632,0.3776072,0.02834835,0.01101035,0.0004963562],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.142961,0.003077688,0.8445176,0.0003927429,0.0005184783,0.0008237447,0.001241774,0.004354031,0.002112764],"genre_scores_gemma":[0.6621671,0.00108142,0.3311751,0.0004618777,0.0001401604,0.001622911,0.002239587,0.0004631538,0.0006486653],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03907383,"threshold_uncertainty_score":0.2066447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06811592733085549,"score_gpt":0.2983876165939409,"score_spread":0.2302716892630854,"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."}}