{"id":"W1970995125","doi":"10.1145/2506583.2512380","title":"Sparse and Stable Reconstruction of Genetic Regulatory Networks Using Time Series Gene Expression Data","year":2013,"lang":"en","type":"article","venue":"","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Gene regulatory network; Computer science; Series (stratigraphy); Stability (learning theory); Gene; Genetic algorithm; Network topology; Expression (computer science); Computational biology; Set (abstract data type); Regular polygon; Regulation of gene expression; Time series; Gene expression; Biology; Mathematics; Genetics; Machine learning","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":[0.0001406248,0.000143473,0.0001925483,0.00004269684,0.00007507289,0.00002463237,0.0001912104,0.0001521017,0.0002161639],"category_scores_gemma":[0.00001217032,0.000132624,0.00003827521,0.00009309575,0.00014007,0.00002228487,0.0003745614,0.00004154341,0.000004000552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007800189,"about_ca_system_score_gemma":0.00003883627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000498039,"about_ca_topic_score_gemma":0.00001219115,"domain_scores_codex":[0.998933,0.00007076975,0.000276404,0.000420868,0.0000998921,0.0001990907],"domain_scores_gemma":[0.998749,0.000005065695,0.0001446236,0.0009236326,0.00009257731,0.00008510739],"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":[0.00001534407,0.00001195374,0.01232659,0.00001019441,0.00006381658,3.335639e-7,0.000004004945,0.01031839,0.9710476,0.00000179436,0.001684434,0.004515557],"study_design_scores_gemma":[0.0003953916,0.0001253196,0.01975761,0.00004128519,0.0001459743,0.0001441988,0.00008346806,0.1581311,0.8197897,0.0001056413,0.0008729466,0.0004073229],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9872417,0.00193733,0.01047237,0.00001183572,0.00006171248,0.00014261,0.000007249796,0.00001041902,0.0001147026],"genre_scores_gemma":[0.9411144,0.0003970464,0.0572103,0.0000213146,0.0002078288,0.000004545979,0.0001320417,0.00002429585,0.000888174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1512579,"threshold_uncertainty_score":0.5408248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01324984551645808,"score_gpt":0.2127405516563206,"score_spread":0.1994907061398625,"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."}}