{"id":"W2000692739","doi":"10.1089/cmb.2008.0087","title":"Heuristic Approach to Sparse Approximation of Gene Regulatory Networks","year":2008,"lang":"en","type":"article","venue":"Journal of Computational Biology","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Heuristic; Gene regulatory network; Computational biology; Computer science; Gene; Biology; Mathematics; Artificial intelligence; Mathematical optimization; Genetics; Gene expression","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003406626,0.0001235313,0.0003063224,0.0001595509,0.00005590938,0.000003421634,0.0002049461,0.0001389843,0.000005634755],"category_scores_gemma":[0.00006524877,0.0001116352,0.0001847225,0.0001995647,0.0001267416,0.000004207802,0.0000448837,0.00008819644,0.000002044239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001968785,"about_ca_system_score_gemma":0.0001290954,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001183222,"about_ca_topic_score_gemma":3.325277e-7,"domain_scores_codex":[0.9987767,0.0001430843,0.000577858,0.0001780252,0.0001695547,0.0001547786],"domain_scores_gemma":[0.9987282,0.00003475576,0.0004897767,0.0001657923,0.0004754418,0.0001060497],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0001371209,0.0001271154,0.003829286,0.000006928018,0.0002008485,0.000002635777,0.00003989425,0.9503121,0.04158689,0.0004517505,0.00214875,0.001156655],"study_design_scores_gemma":[0.00644519,0.005275602,0.5110301,0.0001038997,0.0006628055,0.006029307,0.0002014261,0.3316471,0.1141879,0.01120329,0.01140224,0.001811148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5804891,0.0007389241,0.4184271,0.00003522029,0.0000981481,0.00005779141,0.00000328753,0.000002057379,0.0001484076],"genre_scores_gemma":[0.9451771,0.00005110911,0.05400947,0.0001337539,0.0004682367,0.000002391761,0.0001064295,0.00001206547,0.00003937918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.618665,"threshold_uncertainty_score":0.4552351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01454337313755146,"score_gpt":0.2369159129839828,"score_spread":0.2223725398464313,"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."}}