{"id":"W1980652169","doi":"","title":"Hierarchical Cluster analysis of SAGE data for cancer profiling","year":2001,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of British Columbia","funders":"","keywords":"Cluster analysis; Computer science; Preprocessor; Data mining; Clustering high-dimensional data; Hierarchical clustering; Data pre-processing; Subspace topology; Artificial intelligence; Pattern recognition (psychology)","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":[0.0001147048,0.0000519455,0.00009624207,0.00005642595,0.0000240697,0.000006032719,0.0002077535,0.00005851896,0.00009451113],"category_scores_gemma":[0.00003123652,0.00004179821,0.00006025891,0.0001823956,0.0000227668,0.000002054321,0.00009413205,0.00002185838,3.075236e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000039991,"about_ca_system_score_gemma":0.00004252537,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000175136,"about_ca_topic_score_gemma":0.000121268,"domain_scores_codex":[0.9994361,0.00001786532,0.0001344149,0.0002563685,0.00006379279,0.00009148144],"domain_scores_gemma":[0.9993412,0.00000791729,0.00004945726,0.0005223614,0.00004563097,0.00003342254],"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":[0.0001692887,0.00004915141,0.0125853,0.00001275754,0.0003487315,8.078153e-8,0.00001247269,0.0003909218,0.9585763,0.0001402001,0.006311394,0.02140343],"study_design_scores_gemma":[0.0008272353,0.00009692689,0.01134097,0.0000103944,0.0005400304,8.217872e-7,0.0001091421,0.04230969,0.5016994,0.00006107255,0.4427899,0.0002143784],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.623832,0.0007752579,0.3708034,0.001357571,0.0001285706,0.000338756,0.0003716184,0.00001130661,0.002381511],"genre_scores_gemma":[0.9905127,0.0003932412,0.004034309,0.0004558409,0.0001158398,0.00005148529,0.001360664,0.000007491777,0.003068486],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4568768,"threshold_uncertainty_score":0.1704482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04423730316158471,"score_gpt":0.3483619849807667,"score_spread":0.304124681819182,"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."}}