{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003828966,0.0003107076,0.0003459032,0.0007600326,0.0001861872,0.0005375327,0.0003111665,0.0002963908,0.0004558416],"category_scores_gemma":[0.002902218,0.0002320857,0.000323504,0.0007202455,0.0004225846,0.0004956697,0.0003705044,0.0005031056,0.00009644981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005969731,"about_ca_system_score_gemma":0.0004582288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003015825,"about_ca_topic_score_gemma":0.003697384,"domain_scores_codex":[0.9998044,0.0000441018,0.000007669812,0.00008318682,0.00003737862,0.00002321609],"domain_scores_gemma":[0.9989628,0.0006492044,0.0001767696,0.00009486623,0.00006142843,0.00005496336],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003524329,0.0001089032,0.04760991,0.0001054278,0.0001209345,0.0003025574,0.0001230629,0.7413334,0.1507728,0.01665789,0.0006330702,0.04187963],"study_design_scores_gemma":[0.000005693119,0.00002335412,0.01319278,0.000003991237,0.00001370621,0.0000401291,0.00003041656,0.963985,0.0127932,0.009560184,0.0003382545,0.00001336309],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6752834,0.0001523745,0.3221421,0.0001295117,0.000009541397,0.00001801495,0.0007105505,0.0003956156,0.001158762],"genre_scores_gemma":[0.9734397,0.0001112038,0.02565047,0.00001874346,0.000009082868,0.00002764757,0.0005116338,0.00003529359,0.0001962711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003015825,"threshold_uncertainty_score":0.005996525,"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."}}