{"id":"W2096482937","doi":"10.1093/bioinformatics/btv321","title":"ABC: a tool to identify SNVs causing allele-specific transcription factor binding from ChIP-Seq experiments","year":2015,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University Health Network; University of Toronto; Ontario Institute for Cancer Research","funders":"National Cancer Institute; Canadian Institutes of Health Research","keywords":"Perl; Computational biology; Computer science; Biology; SNP; False positive paradox; Chromatin; Genetics; Transcription factor; Single-nucleotide polymorphism; Gene; Artificial intelligence; Programming language","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000124672,0.0002232323,0.0001822721,0.00007428211,0.0001022795,0.000164503,0.0002132379,0.0001750157,0.00003051963],"category_scores_gemma":[0.0000277565,0.0002247358,0.0000935201,0.00009458543,0.00002713055,0.00002241836,0.0001181346,0.00008113384,0.0001409668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008785385,"about_ca_system_score_gemma":0.00007086464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002151732,"about_ca_topic_score_gemma":0.00001409062,"domain_scores_codex":[0.9987532,0.00001852254,0.0004595665,0.0002132395,0.0002428212,0.0003126358],"domain_scores_gemma":[0.9991743,0.000004555375,0.0001342024,0.0004047789,0.00008370774,0.0001984535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00005627383,0.00005067897,0.0003913002,0.00002095877,0.0000623634,0.000002581061,0.003346328,0.0002481857,0.988731,0.00003789261,0.003796094,0.003256372],"study_design_scores_gemma":[0.005770816,0.001047519,0.01116047,0.0001707343,0.00009347039,0.00005466961,0.01115619,0.02836684,0.7168056,0.0003472096,0.2223179,0.00270863],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9745359,0.0001949255,0.02365614,0.0000448596,0.0005186244,0.000286834,0.0001963256,0.00002775283,0.000538588],"genre_scores_gemma":[0.9656095,0.00008886647,0.03271219,0.0002314125,0.0002287525,0.00001810655,0.0008973336,0.00003498875,0.000178865],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2719254,"threshold_uncertainty_score":0.9164462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03939331742921866,"score_gpt":0.2816686085563228,"score_spread":0.2422752911271041,"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."}}