{"id":"W2095435642","doi":"10.1109/tcbb.2015.2424421","title":"Probabilistic Inference on Multiple Normalized Signal Profiles from Next Generation Sequencing: Transcription Factor Binding Sites","year":2015,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Chinese University of Hong Kong; University of Hong Kong; City University of Hong Kong","keywords":"Chromatin immunoprecipitation; ENCODE; Enhancer; Computational biology; Inference; Probabilistic logic; DNA sequencing; Computer science; Conditional independence; Transcription factor; Genome; DNA binding site; Gene; Biology; Genetics; Promoter; Artificial intelligence; Gene expression","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.005837487,0.001380523,0.001511069,0.002582038,0.0005994327,0.001537166,0.002246702,0.001408016,0.002357749],"category_scores_gemma":[0.01483949,0.001160771,0.002554136,0.002115089,0.001077963,0.001612662,0.001045465,0.002615765,0.001020966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009510195,"about_ca_system_score_gemma":0.001626854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004943018,"about_ca_topic_score_gemma":0.007481944,"domain_scores_codex":[0.9973277,0.001091683,0.0001292252,0.0007315608,0.0005661799,0.0001536358],"domain_scores_gemma":[0.9927496,0.005391862,0.0005612761,0.0006680719,0.0004664187,0.0001627644],"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":[0.000799669,0.000211645,0.01300148,0.000699254,0.0006448917,0.0004281377,0.0001910874,0.6961339,0.02730514,0.0222782,0.005364305,0.2329422],"study_design_scores_gemma":[0.00003167528,0.00002102821,0.001048311,0.00001464073,0.00003131211,0.00005539313,0.00001169346,0.9730538,0.004456817,0.02019189,0.001054127,0.00002928069],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01113522,0.0002501488,0.9861779,0.0001353884,0.00003180954,0.00003185909,0.0005175781,0.001501578,0.0002184278],"genre_scores_gemma":[0.2725635,0.0006604369,0.7172009,0.0005220163,0.0001825312,0.000285281,0.006152337,0.0005793708,0.001853487],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005837487,"threshold_uncertainty_score":0.03087193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07954189857570151,"score_gpt":0.278009197832055,"score_spread":0.1984672992563535,"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."}}