{"id":"W2150038257","doi":"10.1111/j.1541-0420.2010.01441.x","title":"PICS: Probabilistic Inference for ChIP-seq","year":2010,"lang":"en","type":"article","venue":"Biometrics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":71,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Montreal Clinical Research Institute; BC Cancer Agency; University of British Columbia","funders":"","keywords":"False discovery rate; Chromatin immunoprecipitation; Computer science; Inference; DNA binding site; Probabilistic logic; Computational biology; Statistical model; Bayesian probability; Bayesian inference; Synthetic data; Event (particle physics); Data mining; Algorithm; Biology; Artificial intelligence; Genetics; Promoter; 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":[0.0001818466,0.0001181153,0.0001005472,0.0001698076,0.00006999308,0.00004387754,0.0002561709,0.0001825189,0.0000121039],"category_scores_gemma":[0.0009724037,0.0001129271,0.00007329709,0.0004339346,0.00006129098,0.000001641761,0.00009441411,0.00007912691,0.00001089038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009236624,"about_ca_system_score_gemma":0.00008618936,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004568587,"about_ca_topic_score_gemma":0.00002977097,"domain_scores_codex":[0.9992601,0.000006918565,0.0001659182,0.0002628741,0.00008640384,0.000217758],"domain_scores_gemma":[0.9993008,0.00004082555,0.00007414894,0.0003594065,0.000142319,0.00008243791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001686495,0.0001076912,0.003705724,0.00005903034,0.00002537324,4.703368e-7,0.00001574527,0.00002760712,0.9778709,0.003778891,0.001004588,0.01338708],"study_design_scores_gemma":[0.003581235,0.002461785,0.063726,0.00002222003,0.000140114,0.00004253391,0.00009949515,0.01957062,0.1990069,0.01875902,0.6904528,0.002137319],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9838233,0.0001142783,0.01439867,0.00006412974,0.0005884027,0.0002769722,0.00009961516,0.00001490493,0.0006197009],"genre_scores_gemma":[0.9814348,0.00004834377,0.01745338,0.0000952444,0.0002714776,0.00004047071,0.0002098571,0.00002186394,0.0004246125],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.778864,"threshold_uncertainty_score":0.4605033,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273253963966695,"score_gpt":0.2645163773343264,"score_spread":0.2517838376946594,"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."}}