{"id":"W4282011047","doi":"10.1186/s13059-022-02690-2","title":"Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome","year":2022,"lang":"en","type":"article","venue":"Genome biology","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Vector Institute; Princess Margaret Cancer Centre; University of Toronto","funders":"Banff International Research Station for Mathematical Innovation and Discovery; University of Toronto; Canadian Cancer Society","keywords":"Biology; Computational biology; Transcription factor; DNA binding site; Sequence (biology); Binding site; Genetics; Chromatin; Sequence motif; Transcriptome; Gene; Gene expression; Promoter","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":[0.001511067,0.001439812,0.001281946,0.0008687674,0.0003772076,0.0009728622,0.001436121,0.000664486,0.002833438],"category_scores_gemma":[0.002104146,0.00075966,0.001156035,0.0009043558,0.0006118848,0.0008415014,0.000848916,0.001117101,0.001413096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005468641,"about_ca_system_score_gemma":0.001031028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003537663,"about_ca_topic_score_gemma":0.007297892,"domain_scores_codex":[0.9995787,0.0001042725,0.00002175651,0.0001621539,0.00009384709,0.00003917893],"domain_scores_gemma":[0.9987866,0.000828725,0.00006806242,0.0001568147,0.00009630901,0.00006346548],"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":[0.00127554,0.0005716599,0.03792433,0.001179752,0.001345521,0.0002505093,0.000159794,0.3846007,0.3528409,0.005298871,0.01322052,0.201332],"study_design_scores_gemma":[0.00004182449,0.00009845262,0.004951621,0.0000129941,0.00006761524,0.00009207949,0.00002844503,0.9466346,0.04059536,0.005188358,0.002249432,0.00003928256],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1544315,0.0007158088,0.8176256,0.0001562751,0.00009762318,0.0001355452,0.01012485,0.01508884,0.001623919],"genre_scores_gemma":[0.4799068,0.0006336425,0.4927222,0.0005575435,0.00005937675,0.0007328265,0.02270665,0.0009765704,0.001704424],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003537663,"threshold_uncertainty_score":0.009478748,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009674003886454572,"score_gpt":0.2073851014318302,"score_spread":0.1977110975453756,"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."}}