{"id":"W2898780495","doi":"10.7717/peerj.5854","title":"Ensemble learning for detecting gene-gene interactions in colorectal cancer","year":2018,"lang":"en","type":"article","venue":"PeerJ","topic":"Genetic Associations and Epidemiology","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Genome-wide association study; Single-nucleotide polymorphism; Missing heritability problem; Heritability; Genetic association; Colorectal cancer; Biology; Genetics; Gene; Computational biology; Pairwise comparison; Disease; Cancer; Bioinformatics; Medicine; Computer science; Genotype; Internal medicine; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002469231,0.00007697344,0.0001129986,0.0000371975,0.0001349499,0.000008296686,0.00006736388,0.00007487669,0.00002568005],"category_scores_gemma":[0.0005038509,0.00008047331,0.00005695199,0.00006481511,0.00003137837,0.000001814186,0.00004422493,0.00007727063,0.000009446348],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003371752,"about_ca_system_score_gemma":0.00004901957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002354512,"about_ca_topic_score_gemma":0.003051385,"domain_scores_codex":[0.9993002,0.00005350665,0.0001595734,0.0002200488,0.00003312876,0.0002334768],"domain_scores_gemma":[0.9996559,0.00004778302,0.00007605674,0.00007505505,0.0001120995,0.00003307778],"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":[0.00003552354,0.00001499461,0.09445969,0.000002676962,0.00002452598,2.080708e-7,0.000108098,0.000743346,0.8981453,0.000004579579,0.0007333322,0.005727696],"study_design_scores_gemma":[0.0005333663,0.0003126154,0.1260809,0.000006442336,0.00001741777,0.00001117472,0.0001772118,0.004740379,0.8377089,0.0001416489,0.03008818,0.000181784],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878209,0.0001539931,0.01071132,0.0002635084,0.0003287619,0.0001324347,0.00000830557,0.000009632558,0.0005711931],"genre_scores_gemma":[0.9935583,0.00004233031,0.00444634,0.0001262367,0.0005989547,0.0001304368,0.00005286819,0.00001422858,0.00103025],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06043642,"threshold_uncertainty_score":0.3281606,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02394734885520244,"score_gpt":0.3265189747809241,"score_spread":0.3025716259257217,"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."}}