{"id":"W2153184834","doi":"10.1109/cca.2005.1507116","title":"Genetic network inference via gene set stochastic sampling and sensitivity analysis","year":2005,"lang":"en","type":"article","venue":"","topic":"Gene Regulatory Network Analysis","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Generalizability theory; Robustness (evolution); Inference; Computer science; Artificial neural network; Gene regulatory network; Set (abstract data type); Artificial intelligence; Machine learning; Computational biology; Data mining; Gene; Gene expression; Biology; Genetics; Mathematics; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006654829,0.0009648256,0.001207439,0.001681154,0.0005340506,0.0009272573,0.00112032,0.001091497,0.001296217],"category_scores_gemma":[0.02862428,0.0006954044,0.001134254,0.0006177495,0.001200206,0.001187245,0.001237049,0.001405194,0.0001291069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002259613,"about_ca_system_score_gemma":0.001048066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008755297,"about_ca_topic_score_gemma":0.005803487,"domain_scores_codex":[0.9975218,0.001693041,0.0000680775,0.0002875418,0.0003233959,0.0001060615],"domain_scores_gemma":[0.977101,0.02112892,0.0005849366,0.0005422581,0.0005212583,0.0001215819],"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":[0.00003592531,0.00001599413,0.0005991402,0.00001412562,0.0000389757,0.00002187903,0.00001303524,0.9910684,0.0003315532,0.004424134,0.00005079962,0.003386022],"study_design_scores_gemma":[0.000002446538,0.000003710952,0.000065555,0.000001426555,0.000002723185,0.000002712252,0.000001056334,0.9971135,0.0001615999,0.002618281,0.00002463932,0.000002391403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08068834,0.0001762423,0.9172022,0.0002278012,0.00001461746,0.00009322556,0.0001173957,0.0003905016,0.001089704],"genre_scores_gemma":[0.8903536,0.0001226443,0.1081955,0.0001188236,0.00002437617,0.0002200263,0.0002238318,0.00005575389,0.0006854308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008755297,"threshold_uncertainty_score":0.03519452,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01234391877744657,"score_gpt":0.2544972658704683,"score_spread":0.2421533470930218,"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."}}