{"id":"W4285805265","doi":"10.1145/3520304.3529027","title":"Genetic heterogeneity analysis using genetic algorithm and network science","year":2022,"lang":"en","type":"article","venue":"Proceedings of the Genetic and Evolutionary Computation Conference Companion","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Feature selection; Genome-wide association study; Computer science; Cluster analysis; Selection (genetic algorithm); Feature (linguistics); Genetic association; Data mining; Artificial intelligence; Computational biology; Gene; Genetics; Biology; Single-nucleotide polymorphism","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":[0.0002473229,0.0001556408,0.0001980039,0.0001110138,0.0008049215,0.0000623926,0.0003068733,0.00004832425,0.000008118091],"category_scores_gemma":[0.000009630463,0.0001451143,0.00007359034,0.0006047551,0.0004158012,0.00001265078,0.0008324987,0.0001065134,2.300109e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003711961,"about_ca_system_score_gemma":0.000132525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002430756,"about_ca_topic_score_gemma":0.000001498939,"domain_scores_codex":[0.9987032,0.00002686694,0.0003412262,0.0003668402,0.0003019943,0.0002598104],"domain_scores_gemma":[0.9992311,0.000009790077,0.0002813317,0.000118768,0.0002691741,0.00008983888],"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":[0.00005504158,0.00008318699,0.1707142,0.00006810875,0.0003055396,3.894584e-7,0.0003906164,0.7699055,0.02378061,0.0004452301,0.0004881571,0.03376339],"study_design_scores_gemma":[0.0001788727,0.00009570931,0.3884914,0.000006967897,0.0001171067,0.00006828021,0.0001455104,0.6084782,0.0001757569,0.001988956,0.0001165251,0.0001366396],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9009771,0.001526265,0.09699913,0.0000453087,0.0001477658,0.0002246028,0.00001700591,0.000007416787,0.00005538759],"genre_scores_gemma":[0.9224955,0.0001259749,0.07716514,0.00007409101,0.00009498683,0.00001164638,0.00001043773,0.000008588919,0.00001355914],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2177772,"threshold_uncertainty_score":0.6190886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01402920188400439,"score_gpt":0.2373524050088979,"score_spread":0.2233232031248935,"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."}}