{"id":"W2127224364","doi":"10.1109/ainaw.2007.97","title":"Application of Double Clustering to Gene Expression Data for Class Prediction","year":2007,"lang":"en","type":"article","venue":"","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Cluster analysis; Computer science; Support vector machine; Data mining; Noise (video); Class (philosophy); Data set; Pattern recognition (psychology); Artificial intelligence; Set (abstract data type); Transformation (genetics); Binary data; Expression (computer science); Binary number; Mathematics; 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":[0.0002937854,0.00006555409,0.00006381299,0.00004463178,0.00004082158,0.000006362601,0.0002373423,0.00008809044,0.000005206868],"category_scores_gemma":[0.00001742924,0.00005990172,0.00002316515,0.00007468418,0.00001052689,0.000005078884,0.0001577187,0.00001986293,0.00000206644],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001003239,"about_ca_system_score_gemma":0.00002057785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007265975,"about_ca_topic_score_gemma":0.00002710574,"domain_scores_codex":[0.9992624,0.000006299303,0.0002013959,0.0003242399,0.00009098898,0.0001146841],"domain_scores_gemma":[0.9990708,0.000005374008,0.00007397449,0.0007008551,0.00008675466,0.00006219182],"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":[0.0003552091,0.00002848543,0.0003919523,0.00001474965,0.00000478673,1.387177e-8,0.00001204393,0.0002122546,0.9828871,0.00006002168,0.006825581,0.009207797],"study_design_scores_gemma":[0.0004654755,0.00007644368,0.001129507,0.000005991496,0.000004826545,7.458907e-7,0.00005499297,0.001722498,0.8402503,0.000009705922,0.1562234,0.00005611116],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0619721,0.00005207417,0.9361609,0.0001146128,0.0001257283,0.0004214578,0.00004835544,0.0000138939,0.001090908],"genre_scores_gemma":[0.9738759,0.0000167503,0.02413758,0.0001226197,0.0002378621,0.00006922719,0.0009645351,0.00001241291,0.0005631144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9120233,"threshold_uncertainty_score":0.2442721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03660729115935286,"score_gpt":0.3212023556237346,"score_spread":0.2845950644643818,"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."}}