{"id":"W2129162350","doi":"10.1016/j.compbiomed.2011.11.011","title":"Missing value imputation in DNA microarrays based on conjugate gradient method","year":2011,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Imputation (statistics); Missing data; Mathematics; Conjugate gradient method; Bayesian probability; Mean squared error; Statistics; Iterated function; Computer science; Pattern recognition (psychology); Algorithm; Artificial intelligence","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":[0.0003389776,0.00009631286,0.0001353652,0.0001337928,0.00002854076,0.000001776669,0.00007500045,0.0001219573,0.000007509056],"category_scores_gemma":[0.00003867959,0.00007691334,0.00001601708,0.00008394431,0.0001118959,0.000001526515,0.00002247411,0.00007890395,6.23521e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001438668,"about_ca_system_score_gemma":0.00002524331,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003445392,"about_ca_topic_score_gemma":0.00001043625,"domain_scores_codex":[0.9992017,0.0001646933,0.0001787815,0.0002927913,0.00002928062,0.0001327718],"domain_scores_gemma":[0.9997072,0.00002916101,0.00005884248,0.0001378264,0.00001664836,0.00005034893],"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":[0.0003587243,0.0001005721,0.01684759,0.00002414032,0.0000112098,0.000006474773,0.0007674188,0.0001451429,0.9090053,0.001930804,0.0008885546,0.06991404],"study_design_scores_gemma":[0.01432129,0.004873822,0.3697399,0.0009032553,0.00004569091,0.00005384775,0.0007492432,0.05128323,0.5128179,0.01796293,0.02633687,0.0009119818],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6923333,0.0009750414,0.3013003,0.001986558,0.0008554637,0.0002699542,0.000002379586,0.00001396543,0.002263066],"genre_scores_gemma":[0.9841561,0.0001106197,0.0134259,0.002135225,0.00008044851,0.00001145747,0.00006042125,0.000005876757,0.0000139543],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3961874,"threshold_uncertainty_score":0.3136435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02667661788383582,"score_gpt":0.3284641956446476,"score_spread":0.3017875777608118,"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."}}