{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003105761,0.00006661983,0.00009949032,0.0001890456,0.00002478661,0.000009026781,0.00008299656,0.00008200372,0.00002214438],"category_scores_gemma":[0.00001811828,0.00007295148,0.00002169615,0.000206741,0.00009328772,0.000002750108,0.00005390472,0.00009708432,0.000001322826],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000226986,"about_ca_system_score_gemma":0.00004171407,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001322431,"about_ca_topic_score_gemma":0.00008875492,"domain_scores_codex":[0.9991874,0.00005104697,0.0001539558,0.0002046457,0.0001206416,0.0002822726],"domain_scores_gemma":[0.9997529,0.000009848853,0.00001312607,0.0001282541,0.0000507025,0.00004518579],"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":[0.00009486574,0.00001763719,0.002197994,0.00005396942,0.000005101989,0.000001997431,0.0005783417,0.00004019978,0.9964624,5.686909e-7,0.000005146934,0.0005417591],"study_design_scores_gemma":[0.0003218392,0.00007853694,0.01576703,0.00001738615,7.206318e-7,0.000002526227,0.0003406177,0.000479869,0.9816815,0.000006542558,0.001228037,0.00007538685],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975829,0.0007337013,0.001247901,0.00001015684,0.00003633519,0.0001357056,0.00002018445,0.00000167999,0.000231426],"genre_scores_gemma":[0.9982592,0.0001569493,0.001420993,0.000003928435,0.00007971949,0.000004129009,0.000004081709,0.00001023358,0.00006078236],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01478091,"threshold_uncertainty_score":0.2974875,"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."}}