{"id":"W3212711457","doi":"10.5281/zenodo.1066928","title":"Virtual ChIP-seq software for predicting transcription factor binding by learning from the transcriptome","year":2018,"lang":"en","type":"article","venue":"Figshare","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Transcriptome; Transcription factor; Computer science; Computational biology; Software; RNA-Seq; World Wide Web; Biology; Data science; Data mining; Gene; Genetics; Gene expression; Operating system","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":[0.002318881,0.002222597,0.001784474,0.001626091,0.0008036091,0.001468629,0.002836369,0.001177607,0.01347352],"category_scores_gemma":[0.002992389,0.001662759,0.002629662,0.001257002,0.000802958,0.001308695,0.001385916,0.002397009,0.004809665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007974986,"about_ca_system_score_gemma":0.001431148,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002346959,"about_ca_topic_score_gemma":0.005354291,"domain_scores_codex":[0.9994053,0.0001295234,0.00004676029,0.0002296466,0.0001428559,0.00004596327],"domain_scores_gemma":[0.998293,0.001258829,0.00007758491,0.000205443,0.0000993796,0.00006575923],"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":[0.005223766,0.00089166,0.02358261,0.005129882,0.004267228,0.001017892,0.0009285801,0.1903321,0.3467754,0.02723454,0.1943496,0.2002669],"study_design_scores_gemma":[0.0004814886,0.0003588414,0.008514895,0.00009975072,0.000408389,0.0003872334,0.0001025006,0.7769818,0.1402639,0.02640973,0.04575754,0.0002339853],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.03804925,0.0006625624,0.6972092,0.0002165617,0.000252291,0.0002802471,0.04842819,0.2112843,0.00361744],"genre_scores_gemma":[0.1530443,0.0007923667,0.7191522,0.0007706102,0.00008750999,0.003518881,0.09821603,0.01905694,0.005361049],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.01347352,"threshold_uncertainty_score":0.04507339,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03022082438490258,"score_gpt":0.2445418180973956,"score_spread":0.214320993712493,"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."}}