{"id":"W1982915164","doi":"10.1371/journal.pone.0085864","title":"Pooled Screening for Synergistic Interactions Subject to Blocking and Noise","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa; Ottawa Hospital; McGill University","funders":"Ottawa Hospital Research Institute","keywords":"Pooling; Set (abstract data type); Computer science; Noise (video); Replication (statistics); Bayesian probability; Blocking (statistics); Computational biology; Biology; Statistics; Artificial intelligence; Mathematics","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":[0.02528646,0.001891659,0.003598392,0.001662911,0.0007041002,0.001580519,0.002451368,0.001889016,0.002384342],"category_scores_gemma":[0.05851037,0.001313441,0.002596045,0.001088333,0.001994116,0.002162982,0.002981924,0.001469076,0.0004059186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001162001,"about_ca_system_score_gemma":0.002714531,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009256653,"about_ca_topic_score_gemma":0.001372259,"domain_scores_codex":[0.9806216,0.01202221,0.0008698664,0.003387712,0.00241842,0.0006801878],"domain_scores_gemma":[0.9317586,0.05272235,0.005313485,0.007000709,0.002235514,0.0009693658],"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":[0.006362054,0.002222583,0.01768135,0.001258834,0.001964713,0.0005601599,0.0006381329,0.4193378,0.3044058,0.04062824,0.0007596568,0.2041806],"study_design_scores_gemma":[0.0004952985,0.007265712,0.00546744,0.00008976668,0.0006104489,0.0002864168,0.0001097974,0.8125716,0.1114294,0.05952335,0.001941385,0.0002093529],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1216345,0.0003054796,0.875845,0.0001465444,0.00002296758,0.0004655227,0.0001453124,0.000618459,0.0008162126],"genre_scores_gemma":[0.5060758,0.0002282773,0.4906868,0.0002171385,0.00002559606,0.001411695,0.0003515788,0.000105266,0.0008979505],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02528646,"threshold_uncertainty_score":0.1337292,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02499952631398698,"score_gpt":0.2429412617125458,"score_spread":0.2179417353985589,"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."}}