{"id":"W2796029694","doi":"10.1101/168419","title":"Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptome","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Genomics and Chromatin Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Transcription factor; Computational biology; DNA binding site; Biology; Chromatin; Binding site; Epigenomics; Genetics; Computer science; Bioinformatics; Gene; Gene expression; Promoter","routes":{"ca_aff":true,"ca_fund":false,"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":[0.001486538,0.001328823,0.0008620922,0.001012555,0.0004491365,0.001115023,0.001434632,0.0008213831,0.004904372],"category_scores_gemma":[0.002306042,0.0006890056,0.001290018,0.0008716688,0.0005760847,0.000730939,0.0009243102,0.001132844,0.00231311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007417225,"about_ca_system_score_gemma":0.00110029,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006139177,"about_ca_topic_score_gemma":0.01123399,"domain_scores_codex":[0.9994449,0.0001099814,0.00002710534,0.0002761143,0.00009142322,0.00005038778],"domain_scores_gemma":[0.9990489,0.0006050427,0.00005917384,0.0001353441,0.0001004405,0.00005112188],"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":[0.001940587,0.0004144871,0.04777892,0.001899189,0.00128299,0.0003349983,0.0001836439,0.5827571,0.1289476,0.005353804,0.08328997,0.1458168],"study_design_scores_gemma":[0.00007931525,0.0001066733,0.007827261,0.00004698081,0.0001103119,0.00007150476,0.00005506449,0.9454036,0.02799469,0.007885079,0.01036019,0.00005926281],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2476755,0.002400292,0.5338485,0.0008219586,0.0004546916,0.000323235,0.149159,0.05994399,0.005372727],"genre_scores_gemma":[0.4050468,0.00100527,0.3562025,0.001081996,0.00010035,0.0009995699,0.2284866,0.002796135,0.004280827],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006139177,"threshold_uncertainty_score":0.01640671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009580772913492306,"score_gpt":0.1989916704470095,"score_spread":0.1894108975335173,"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."}}