{"id":"W1998099667","doi":"10.1049/iet-syb.2013.0060","title":"Properties of sparse penalties on inferring gene regulatory networks from time‐course gene expression data","year":2014,"lang":"en","type":"article","venue":"IET Systems Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Gene expression; Computational biology; Gene regulatory network; Gene; Regulation of gene expression; Gene expression profiling; Biology; Genetics; Computer science","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006529251,0.000307914,0.0005655893,0.00007085674,0.0001026675,0.00002111914,0.0007595194,0.0004473121,0.000018092],"category_scores_gemma":[0.00007189161,0.0002482682,0.0001192784,0.00009088134,0.0002160482,0.000008173993,0.0005364918,0.0001240507,0.00002716909],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000168957,"about_ca_system_score_gemma":0.0000597765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001295757,"about_ca_topic_score_gemma":0.00002728839,"domain_scores_codex":[0.9975174,0.0005655197,0.0005655503,0.0008094088,0.0001606584,0.0003814278],"domain_scores_gemma":[0.99722,0.00003387442,0.0003694785,0.002145324,0.0001182659,0.0001130343],"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":[0.0001079489,0.00005566534,0.00718617,0.00002025368,0.0002227761,0.000001009891,0.00002078649,0.01828218,0.9709158,0.00002226337,0.002491306,0.0006738607],"study_design_scores_gemma":[0.0006811731,0.000362208,0.003814372,0.0002184143,0.0001729505,0.0000152333,0.00005565419,0.04089824,0.9429547,0.00002639442,0.01027814,0.0005225172],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9835007,0.01105208,0.004416154,0.0000242284,0.0005292427,0.0002343295,0.00007907153,0.00003124174,0.0001329667],"genre_scores_gemma":[0.9962112,0.000187739,0.0004255704,0.00006255681,0.001543864,0.00002604714,0.001149942,0.00004590594,0.0003471555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02796108,"threshold_uncertainty_score":0.999997,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02443517666078463,"score_gpt":0.2377035775370188,"score_spread":0.2132684008762342,"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."}}