{"id":"W3206734141","doi":"10.1103/physreve.104.044406","title":"Inferring gene regulation dynamics from static snapshots of gene expression variability","year":2021,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Harvard Medical School","keywords":"Computational biology; Biology; Gene; Regulation of gene expression; Population; Gene regulatory network; Gene expression; Dynamics (music); Systems biology; Genetics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0001498638,0.0001339678,0.0001881451,0.00004476351,0.00006656485,0.000008947906,0.0001672935,0.0001484885,0.0001038178],"category_scores_gemma":[0.00006015223,0.0001613972,0.0001366784,0.000295189,0.00006532043,0.00001008129,0.0001602046,0.00006060494,0.000003300882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006649139,"about_ca_system_score_gemma":0.00009639766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004025763,"about_ca_topic_score_gemma":0.0001056047,"domain_scores_codex":[0.9988755,0.0001875165,0.0001875331,0.0005200128,0.00006516522,0.0001642596],"domain_scores_gemma":[0.998849,0.00003001725,0.0001466802,0.0006990905,0.0001918321,0.00008341332],"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":[0.0000476751,0.00006864523,0.05841227,0.00001261115,0.0001075453,0.00001538776,0.00002202329,0.198503,0.7422174,0.0003864158,0.0000390153,0.0001680163],"study_design_scores_gemma":[0.0004994737,0.00003498995,0.03093417,0.00001692646,0.0001551419,0.000002339836,0.00009092934,0.1280512,0.8370345,0.002847325,0.0001109794,0.0002219967],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8258724,0.00006783747,0.1736779,0.00000865303,0.00005376506,0.00005180008,0.0000315275,0.000009148502,0.0002269283],"genre_scores_gemma":[0.9958711,0.00007154539,0.002935088,0.00001841428,0.00006025631,3.107345e-7,0.0006897866,0.00001414833,0.0003393862],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1707429,"threshold_uncertainty_score":0.6581587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02036652983534157,"score_gpt":0.1709769529913736,"score_spread":0.150610423156032,"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."}}