{"id":"W2054628467","doi":"10.1371/journal.pone.0106479","title":"De-Novo Learning of Genome-Scale Regulatory Networks in S. cerevisiae","year":2014,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine; National Center for Research Resources; National Institute of General Medical Sciences; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; York University; NYU Langone Medical Center; National Institutes of Health; National Science Foundation","keywords":"Gene regulatory network; Computational biology; Computer science; Reverse engineering; Genome; Systems biology; Saccharomyces cerevisiae; Network analysis; Biology; Data mining; Gene; Artificial intelligence; Genetics; Gene expression; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003102524,0.0008330854,0.0005833841,0.000836987,0.0003435213,0.0008266754,0.0008142914,0.0005818895,0.0003368405],"category_scores_gemma":[0.01175391,0.0003930934,0.001081683,0.000632888,0.0005180577,0.001098829,0.0006215877,0.001451739,0.0001570053],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001053314,"about_ca_system_score_gemma":0.001028023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006800101,"about_ca_topic_score_gemma":0.01057399,"domain_scores_codex":[0.9991862,0.0003303718,0.00004758862,0.0002644499,0.0001287677,0.00004260198],"domain_scores_gemma":[0.9926292,0.005298787,0.0004722917,0.0009406644,0.0005693256,0.00008982325],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001528664,0.00008097911,0.009765147,0.0001155112,0.0001251861,0.00006825045,0.00007396607,0.9379313,0.01055222,0.0021627,0.0003328316,0.03863906],"study_design_scores_gemma":[0.000007377134,0.00003220897,0.001483676,0.00000683647,0.00002019101,0.00002519247,0.00002312567,0.9863958,0.008989736,0.002693921,0.0003146749,0.000007225546],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6432026,0.0005965353,0.3519171,0.0003375465,0.00003044533,0.00006102672,0.0009700778,0.002069711,0.0008149429],"genre_scores_gemma":[0.8072048,0.0003603887,0.1880976,0.00007995522,0.00001207735,0.00005217762,0.003619751,0.0001255556,0.0004476571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006800101,"threshold_uncertainty_score":0.01640791,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008901186884722271,"score_gpt":0.1879408518243765,"score_spread":0.1790396649396543,"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."}}