{"id":"W3006297573","doi":"10.1109/icicict46008.2019.8993273","title":"Feature Selection Methods for SNP Analysis","year":2019,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Feature selection; SNP; Selection (genetic algorithm); Single-nucleotide polymorphism; Computer science; Feature (linguistics); Curse of dimensionality; SNP genotyping; Tag SNP; Data mining; Genotyping; Artificial intelligence; Computational biology; Pattern recognition (psychology); Biology; Genetics; Genotype; Gene","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":[0.003384232,0.001563375,0.001854164,0.00319761,0.0004889794,0.001294393,0.001376213,0.001033289,0.00452005],"category_scores_gemma":[0.008069049,0.0003959013,0.001998959,0.004084548,0.000434563,0.0009332098,0.0008919389,0.001426275,0.002667922],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005047062,"about_ca_system_score_gemma":0.0007827597,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002268007,"about_ca_topic_score_gemma":0.00146936,"domain_scores_codex":[0.9972717,0.001066486,0.0002687282,0.0004641471,0.0008191774,0.0001097444],"domain_scores_gemma":[0.9969131,0.001985528,0.0001952544,0.0002063318,0.0006570164,0.00004270969],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001612142,0.00009894792,0.002325559,0.0006726687,0.0005392919,0.0001862027,0.00007271011,0.06908777,0.004899127,0.01121958,0.01933336,0.8914036],"study_design_scores_gemma":[0.0001003464,0.0002303909,0.005643774,0.0002137608,0.0002361745,0.0005345882,0.00007282103,0.8835514,0.006062404,0.05640807,0.04683053,0.0001158415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002828458,0.005086951,0.9883165,0.0003977777,0.0002319848,0.0001367564,0.0007325062,0.001207194,0.001062],"genre_scores_gemma":[0.1267485,0.009002984,0.8502551,0.0005364115,0.00113153,0.001300654,0.005091279,0.0005345728,0.005398795],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00452005,"threshold_uncertainty_score":0.01789773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01633387277081668,"score_gpt":0.3717679765721312,"score_spread":0.3554341038013145,"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."}}