{"id":"W4327575955","doi":"10.1093/bioadv/vbad031","title":"Motif elucidation in ChIP-seq datasets with a knockout control","year":2023,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of New Brunswick; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; University of Toronto; Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Research, Innovation and Science; Princess Margaret Cancer Foundation","keywords":"Chromatin immunoprecipitation; Motif (music); Computational biology; Computer science; Transcription factor; Chromatin; Biology; Artificial intelligence; Genetics; Gene; Promoter; Gene expression","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006941955,0.001768371,0.001264563,0.001769974,0.0009700504,0.001522962,0.001812816,0.001247386,0.00574865],"category_scores_gemma":[0.0212066,0.0008269622,0.001716904,0.001511401,0.0009520547,0.0009584649,0.001439961,0.001854892,0.003191127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007566322,"about_ca_system_score_gemma":0.001676329,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003011815,"about_ca_topic_score_gemma":0.007037056,"domain_scores_codex":[0.9972487,0.0006227747,0.0002592959,0.001268079,0.0004432118,0.0001579673],"domain_scores_gemma":[0.9930606,0.004306423,0.0004288371,0.001313178,0.0006943752,0.0001965766],"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":[0.006260065,0.0008432693,0.07743088,0.005499273,0.003232457,0.001098802,0.0007268926,0.1350009,0.3639845,0.009275546,0.1609355,0.2357119],"study_design_scores_gemma":[0.0008923042,0.0004422974,0.02761909,0.0002553093,0.0005264459,0.0006902483,0.000243646,0.7224051,0.1864068,0.01611595,0.04410954,0.0002932959],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2062412,0.001245586,0.5933807,0.0007046344,0.000560318,0.0006671517,0.06358726,0.1307722,0.002840977],"genre_scores_gemma":[0.2341496,0.0002590335,0.6506737,0.0009586651,0.00008540228,0.00171316,0.09009556,0.02047877,0.001586138],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006941955,"threshold_uncertainty_score":0.036713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004276690614795063,"score_gpt":0.2251856240858826,"score_spread":0.2209089334710875,"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."}}