{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001188043,0.0002239695,0.0001763069,0.0001034185,0.0002182914,0.00007078207,0.0001456889,0.0002390119,0.00002082677],"category_scores_gemma":[0.00007795019,0.0001994277,0.00006863585,0.00008537409,0.0001171697,0.00002928043,0.000007938695,0.0001570485,0.00002273819],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006294854,"about_ca_system_score_gemma":0.0001717162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002321683,"about_ca_topic_score_gemma":0.00006668259,"domain_scores_codex":[0.9989567,0.00006953636,0.000389744,0.0002564029,0.0001326225,0.000194936],"domain_scores_gemma":[0.9992495,0.0001554869,0.0001384219,0.0001830381,0.0001565027,0.000117058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003983593,0.0002506311,0.001731087,0.00007068216,0.0002142704,0.000001476312,0.001426955,0.3045532,0.6712568,0.0002969968,0.0001259311,0.01967361],"study_design_scores_gemma":[0.001686466,0.001207328,0.0007277369,0.00003263919,0.00004495042,0.00001539692,0.0004105274,0.9364191,0.05672263,0.002072843,0.0002214213,0.0004389947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7587569,0.00003228956,0.2400536,0.00008182941,0.0002114183,0.0002417911,0.0005663665,0.00002431131,0.00003146482],"genre_scores_gemma":[0.9649549,0.00004987529,0.03222933,0.0002303847,0.0001119814,0.00003131661,0.002354274,0.00001263616,0.00002536038],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6318659,"threshold_uncertainty_score":0.8132427,"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."}}