{"id":"W2738214912","doi":"10.1515/sagmb-2016-0066","title":"Comparing the performance of linear and nonlinear principal components in the context of high-dimensional genomic data integration","year":2017,"lang":"en","type":"article","venue":"Statistical Applications in Genetics and Molecular Biology","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hamilton Health Sciences; Impact; McMaster University; Population Health Research Institute","funders":"Canadian Institutes of Health Research","keywords":"Context (archaeology); Principal component analysis; Nonlinear system; Computer science; Mathematics; Data mining; Statistics; Econometrics; Biology; Physics","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":[0.0002411478,0.00007455216,0.0001202157,0.0000270124,0.00009400339,0.000009622137,0.0003898049,0.00006375444,9.939192e-7],"category_scores_gemma":[0.00004534909,0.0000491554,0.000009065635,0.00003077708,0.0004146388,0.00000219965,0.0003002661,0.00007916151,2.147673e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003381669,"about_ca_system_score_gemma":0.0000288595,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009816693,"about_ca_topic_score_gemma":0.0001227447,"domain_scores_codex":[0.9993117,0.00007232941,0.0002356968,0.0002364523,0.00005683922,0.00008693374],"domain_scores_gemma":[0.9991458,0.00003551801,0.0001303143,0.0006258151,0.00004262313,0.00001992836],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00008634175,0.0001064307,0.06994534,0.00002597255,0.00001685732,2.69258e-7,0.00009072416,0.00008919449,0.8936633,0.02170887,0.00001785552,0.01424888],"study_design_scores_gemma":[0.00112156,0.0003345459,0.834747,0.00002827231,0.00003276335,0.000007345913,0.0002318826,0.05860702,0.09935614,0.001447798,0.003910156,0.0001754964],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9776415,0.0004843803,0.02125629,0.0001904936,0.00001849832,0.0002661435,0.0001141793,6.706828e-7,0.00002779624],"genre_scores_gemma":[0.9925312,0.0004001482,0.00641746,0.00007064301,0.00001793395,0.00003888613,0.0005169536,0.000004774316,0.000002008575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7943071,"threshold_uncertainty_score":0.2004499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04208724528262083,"score_gpt":0.3307104930972203,"score_spread":0.2886232478145995,"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."}}