{"id":"W3164137849","doi":"10.1038/s41598-021-90353-w","title":"A top-down measure of gene-to-gene coordination for analyzing cell-to-cell variability","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Azrieli Foundation","keywords":"Spurious relationship; Gene expression; Computational biology; Biology; Gene; Biological data; Gene regulatory network; Measure (data warehouse); Biological system; Computer science; Bioinformatics; Data mining; Genetics; Machine learning","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":[0.002229223,0.0001488638,0.0002506429,0.0001202637,0.000171962,0.0000731481,0.0001508602,0.0001175477,0.00003077669],"category_scores_gemma":[0.0003985606,0.0001560908,0.0002626506,0.0008288827,0.00005511711,0.000004532565,0.000124016,0.00003897135,0.000003507152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003708221,"about_ca_system_score_gemma":0.0003662468,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006348082,"about_ca_topic_score_gemma":0.00002795057,"domain_scores_codex":[0.9976934,0.0001015379,0.0005253814,0.001054268,0.0003354189,0.0002899544],"domain_scores_gemma":[0.9971939,0.00001548238,0.0002545248,0.001300052,0.001051948,0.0001840487],"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":[0.00001294025,0.00009437596,0.00713255,0.00003576302,0.00004138953,0.00001026783,0.00005661966,0.006836377,0.9788187,0.000002384749,0.006319427,0.0006391663],"study_design_scores_gemma":[0.0001205146,0.00004322245,0.0006945144,0.000007242197,0.00009107438,0.00001804546,0.00003062779,0.0002382629,0.9812109,0.0002137178,0.01716559,0.0001662753],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9439119,0.0003683403,0.05367069,0.0001218725,0.001158429,0.0003631375,0.00001328234,0.000009301611,0.0003830335],"genre_scores_gemma":[0.9831373,0.00000200837,0.01222506,0.00003780584,0.0001306903,0.00003891749,0.0003250453,0.00001807998,0.004085056],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04144562,"threshold_uncertainty_score":0.6365197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008246968475069333,"score_gpt":0.2325165354368957,"score_spread":0.2242695669618263,"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."}}