{"id":"W1995771052","doi":"10.1038/npre.2010.3913.2","title":"Design of a dynamic model of genes with multiple autonomous regulatory modules by evolution in silico","year":2010,"lang":"en","type":"preprint","venue":"Nature Precedings","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; British Columbia Institute of Technology","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Science Foundation","keywords":"In silico; Benchmark (surveying); Crossover; Computer science; Exploit; Evolutionary algorithm; Genetic algorithm; Computational biology; Artificial intelligence; Gene; Machine learning; Biology; Genetics","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":[0.0003053446,0.000378432,0.000661922,0.0002626497,0.000307619,0.0007179514,0.0009037163,0.000813327,0.001734749],"category_scores_gemma":[0.0004915157,0.0003563736,0.0007292068,0.0002183309,0.0005757155,0.000455391,0.0004669262,0.0005471548,0.0002758201],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006035512,"about_ca_system_score_gemma":0.0005920245,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001319527,"about_ca_topic_score_gemma":0.000845051,"domain_scores_codex":[0.9998684,0.00003762297,0.000005680517,0.00003507451,0.00003741105,0.0000157973],"domain_scores_gemma":[0.9998587,0.00007189147,0.00001870633,0.00001566971,0.00001876016,0.00001620631],"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":[0.00002565207,0.00002174482,0.0002438066,0.00002325009,0.00001522905,0.00005296995,0.00001988236,0.9747025,0.01302423,0.00979719,0.00009000761,0.001983461],"study_design_scores_gemma":[0.00001479839,0.00001700771,0.00004209702,0.000001695633,0.000005931892,0.000009540399,0.000002997543,0.9959214,0.001935718,0.001448964,0.0005960729,0.000003707739],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1973236,0.0001801738,0.7934399,0.0004420259,0.00007237495,0.00006624634,0.0001339602,0.0005734346,0.007768213],"genre_scores_gemma":[0.8986809,0.0002381824,0.0962054,0.00007752433,0.00001358864,0.0002815759,0.0001346335,0.00009781562,0.00427047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001734749,"threshold_uncertainty_score":0.005803347,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007717461571683098,"score_gpt":0.2287124198290217,"score_spread":0.2209949582573386,"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."}}