{"id":"W2950338203","doi":"10.1101/gr.227124.117","title":"Mapping transcription factor occupancy using minimal numbers of cells in vitro and in vivo","year":2018,"lang":"en","type":"article","venue":"Genome Research","topic":"CRISPR and Genetic Engineering","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"H2020 European Research Council; Biotechnology and Biological Sciences Research Council; Medical Research Council Canada; Medical Research Council; University of Edinburgh","keywords":"Biology; Transcription factor; SOX2; Embryonic stem cell; Embryo; Gene; Cell biology; Genetics; In vivo; DNA; Computational biology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005860215,0.0008258821,0.0007545602,0.0005072303,0.0003678534,0.0007646194,0.0007871035,0.0005915017,0.002645116],"category_scores_gemma":[0.0004522878,0.000662545,0.0005840042,0.0003902879,0.0004231106,0.0003546846,0.0007483534,0.001212564,0.001681524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005806676,"about_ca_system_score_gemma":0.0004433488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002026931,"about_ca_topic_score_gemma":0.006557186,"domain_scores_codex":[0.9989937,0.00009164526,0.0001096488,0.0003892099,0.0003161368,0.00009954993],"domain_scores_gemma":[0.9995384,0.0001712594,0.00006795222,0.00009141966,0.00006323656,0.00006777651],"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":[0.00002438298,0.00002060613,0.0001667542,0.00005050263,0.000006360148,0.00001316696,0.00001247027,0.0001490278,0.9974141,0.0001304355,0.00005423052,0.001958049],"study_design_scores_gemma":[0.00001049427,0.0001279052,0.002164549,0.00001153351,0.00002832336,0.00008657754,0.00002453442,0.002774465,0.9875066,0.0001226077,0.007130305,0.00001206235],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4533134,0.005172878,0.5206944,0.0003006456,0.0003444759,0.0004632779,0.00584818,0.001771519,0.0120913],"genre_scores_gemma":[0.6029094,0.00586684,0.3571698,0.0005771104,0.00008080067,0.001714469,0.01654664,0.000753666,0.0143813],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002645116,"threshold_uncertainty_score":0.008848786,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04629733479405013,"score_gpt":0.3707388847993516,"score_spread":0.3244415500053015,"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."}}