{"id":"W4398765605","doi":"10.1101/2024.05.23.594371","title":"Predictive Biophysical Neural Network Modeling of a Compendium of <i>in vivo</i> Transcription Factor DNA Binding Profiles for <i>Escherichia coli</i>","year":2024,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Diffusion and Search Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"National Institutes of Health; Universidad Nacional Autónoma de México","keywords":"Compendium; Escherichia coli; Transcription factor; Computational biology; DNA; Transcription (linguistics); DNA binding site; Biology; Genetics; Chemistry; Promoter; Gene; Gene expression","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003199699,0.0004184751,0.0006189541,0.0001570797,0.00005278537,0.0000497174,0.0003981721,0.0005134303,0.000005474597],"category_scores_gemma":[0.00005929206,0.0004369339,0.0003287159,0.0003111778,0.0001294796,0.000009242765,0.0003699483,0.0004654702,0.000001059515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006290217,"about_ca_system_score_gemma":0.0004176512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002352879,"about_ca_topic_score_gemma":0.000006080751,"domain_scores_codex":[0.9976921,0.0001105771,0.0006511211,0.0007882541,0.0002824181,0.0004755225],"domain_scores_gemma":[0.9987212,0.00002636488,0.0002649213,0.0005279249,0.000319351,0.0001402773],"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":[0.00033044,0.0001975815,0.0005371211,0.001152552,0.0001425522,0.000003117795,0.00001751744,0.009145028,0.9880584,0.0003275668,0.00008720734,9.299206e-7],"study_design_scores_gemma":[0.0006414534,0.0002848611,0.000862862,0.00057179,0.00009329193,8.139731e-9,0.000008624947,0.2962327,0.7006977,0.000008964837,0.0001870156,0.000410802],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900076,0.000397724,0.005175353,0.00004646076,0.0007584678,0.001201392,0.002356998,0.00004816374,0.000007798793],"genre_scores_gemma":[0.9966515,0.0002054795,0.002407908,0.00004652244,0.000354183,0.0002134288,0.000007995153,0.0001063011,0.000006707767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2873607,"threshold_uncertainty_score":0.9998083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01430526841136648,"score_gpt":0.2338312202039925,"score_spread":0.219525951792626,"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."}}