{"id":"W2056992887","doi":"10.1016/j.jbi.2006.04.001","title":"Direct classification of high-dimensional data in low-dimensional projected feature spaces—Comparison of several classification methodologies","year":2006,"lang":"en","type":"article","venue":"Journal of Biomedical Informatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada; National Research Council Institute for Biodiagnostics","funders":"","keywords":"Pattern recognition (psychology); Linear discriminant analysis; Computer science; Visualization; Artificial intelligence; k-nearest neighbors algorithm; Discriminant; Feature (linguistics); Similarity (geometry); Data mining; Image (mathematics)","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":[0.004255185,0.0007964757,0.001386999,0.002265502,0.000535213,0.002639356,0.001187052,0.0008892184,0.002320471],"category_scores_gemma":[0.008786336,0.0003008532,0.00105291,0.001745824,0.0007207852,0.001960191,0.001838509,0.001136507,0.001019251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000487067,"about_ca_system_score_gemma":0.0009536885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00149587,"about_ca_topic_score_gemma":0.002052645,"domain_scores_codex":[0.9975014,0.0008888749,0.0002112024,0.0003192835,0.0009389026,0.0001402686],"domain_scores_gemma":[0.992426,0.004793121,0.0003183695,0.000525147,0.001758842,0.0001785312],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006877476,0.0003065147,0.003727593,0.0003689021,0.0002975719,0.00003312806,0.000161064,0.01726254,0.003854563,0.002921663,0.001414787,0.9689639],"study_design_scores_gemma":[0.0001510648,0.0006388466,0.01190345,0.00007860005,0.0002562184,0.0003185263,0.0003491137,0.9656897,0.007846789,0.01029534,0.002418882,0.00005358044],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07693401,0.002465301,0.9164886,0.000339858,0.0001045444,0.0001849169,0.00020947,0.0008267108,0.002446526],"genre_scores_gemma":[0.4943506,0.0025017,0.4974448,0.0001743327,0.0002469207,0.0002930976,0.00106466,0.0001655745,0.003758363],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004255185,"threshold_uncertainty_score":0.02250385,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05815868702192238,"score_gpt":0.3537840751903323,"score_spread":0.2956253881684099,"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."}}