{"id":"W4232894656","doi":"10.4018/978-1-60960-491-2.ch007","title":"Biclustering of DNA Microarray Data","year":2011,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Biclustering; Computer science; Identification (biology); Microarray analysis techniques; Computational biology; Data mining; Biological data; Microarray databases; Cluster analysis; Microarray; Bioinformatics; Artificial intelligence; Gene; Biology; Genetics","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":[0.0000690188,0.0002184439,0.0002051027,0.00002751046,0.00002848527,0.00001125457,0.0006473026,0.0003623845,0.00004724624],"category_scores_gemma":[0.00000877893,0.0002142268,0.00008976216,0.000006791381,0.00008322798,0.000001510379,0.0005206121,0.00007801317,0.00002458322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001921218,"about_ca_system_score_gemma":0.0001453925,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001819117,"about_ca_topic_score_gemma":0.00003261682,"domain_scores_codex":[0.998936,0.00001097337,0.0002689811,0.0005124754,0.0001276933,0.0001438735],"domain_scores_gemma":[0.9982374,0.00000156536,0.0002361061,0.001378156,0.00007254917,0.00007423759],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001575469,0.00001808031,0.00002722401,0.00009371487,0.0001740611,0.00000417079,0.00002315374,6.759841e-7,0.8206406,0.125325,0.03309682,0.02043892],"study_design_scores_gemma":[0.0003493018,0.0001184896,0.00005044168,0.0001515325,0.00007090766,0.00002016246,0.00000862866,0.000002180897,0.1518321,0.01114441,0.8358579,0.0003939773],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0008844547,0.001272781,0.0008241503,0.00001456693,0.000334324,0.0001582522,0.0003936685,0.00001396046,0.9961038],"genre_scores_gemma":[0.7480948,0.0001829402,0.001008196,0.0003910165,0.0006527248,0.0000135643,0.000433271,0.00008723502,0.2491362],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.8027611,"threshold_uncertainty_score":0.8735914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04400898701187462,"score_gpt":0.2701675585442981,"score_spread":0.2261585715324235,"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."}}