{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007884939,0.001773317,0.001849543,0.00213754,0.0009762411,0.001892601,0.004130942,0.001514607,0.005437727],"category_scores_gemma":[0.03119027,0.00230135,0.00206617,0.001910201,0.001748286,0.001563802,0.002551542,0.003534646,0.002380579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001784182,"about_ca_system_score_gemma":0.002804527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008643864,"about_ca_topic_score_gemma":0.01046644,"domain_scores_codex":[0.9958312,0.001921126,0.0001928895,0.0007921139,0.001146735,0.0001159396],"domain_scores_gemma":[0.9844617,0.01208593,0.0008876227,0.001472717,0.0008234452,0.0002685687],"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":[0.0004471628,0.0001581769,0.008076112,0.0006948664,0.000863185,0.000229752,0.0002506649,0.7109193,0.01177744,0.07510363,0.01566382,0.1758159],"study_design_scores_gemma":[0.00002948012,0.00001606942,0.0004512833,0.00001524969,0.00001984467,0.00004067899,0.000005855264,0.9717606,0.001907846,0.02302229,0.002703713,0.00002701193],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007647626,0.00006476288,0.9950109,0.00003806262,0.00001834679,0.00003931125,0.0002995865,0.003565335,0.000199032],"genre_scores_gemma":[0.05024923,0.0001995272,0.9434947,0.0002451453,0.0000841179,0.0007244234,0.002334903,0.001686488,0.0009813354],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008643864,"threshold_uncertainty_score":0.04170001,"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."}}