{"id":"W2022441134","doi":"10.1093/bioinformatics/btg182","title":"Class prediction and discovery using gene microarray andproteomics mass spectroscopy data: curses, caveats, cautions","year":2003,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":332,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; National Research Council Institute for Biodiagnostics","funders":"","keywords":"Computer science; Curse of dimensionality; Artificial intelligence; Pattern recognition (psychology); Feature (linguistics); Relevance (law); Data mining; Microarray analysis techniques; Identification (biology); Outlier; Machine learning; Biology; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00016134,0.0001427239,0.0001080956,0.00005231334,0.0001624988,0.00009888608,0.0001493769,0.0001380961,0.000004695149],"category_scores_gemma":[0.00003851781,0.00013321,0.00002862935,0.00009610545,0.00007403421,0.00004750143,0.00007251379,0.00008040398,0.000003133748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003383332,"about_ca_system_score_gemma":0.0001647615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005349584,"about_ca_topic_score_gemma":0.000005396958,"domain_scores_codex":[0.9991549,0.00003253181,0.0002742917,0.00022886,0.0001142922,0.0001951745],"domain_scores_gemma":[0.9991248,0.000003783961,0.0001351605,0.0006108661,0.00004454256,0.00008090024],"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":[0.00001734975,0.00002247456,0.001806108,0.00002800284,0.0000245255,2.497175e-7,0.00004507237,0.0001073347,0.9937503,0.0003752942,0.003649843,0.0001734629],"study_design_scores_gemma":[0.0007975999,0.0001135847,0.001028959,0.00003228048,0.00006597338,0.0000646655,0.0004323355,0.01467741,0.8888269,0.000139671,0.09350351,0.0003170893],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4786457,0.001299118,0.5162196,0.0001173228,0.0006932209,0.0004699252,0.0005660174,0.00003167191,0.001957531],"genre_scores_gemma":[0.8009924,0.002766727,0.1923028,0.0003542175,0.0003685787,0.00002756982,0.002315417,0.00004316487,0.0008292151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3239168,"threshold_uncertainty_score":0.5432148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02672686303689213,"score_gpt":0.270721456698394,"score_spread":0.2439945936615018,"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."}}