{"id":"W2119632600","doi":"10.1504/ijdmb.2011.040388","title":"Combining multiple perspective as intelligent agents into robust approach for biomarker detection in gene expression data","year":2011,"lang":"en","type":"article","venue":"International Journal of Data Mining and Bioinformatics","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Perspective (graphical); Gene; Gene expression; Biomarker; Computer science; Computational biology; Expression (computer science); microRNA; Data mining; Biology; Artificial intelligence; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003672507,0.001180944,0.001421329,0.001292674,0.00053805,0.002026777,0.00148793,0.001215386,0.0009410146],"category_scores_gemma":[0.006300414,0.0005310532,0.001427984,0.001060332,0.0009304212,0.002002161,0.001903092,0.001136768,0.0002177673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008634284,"about_ca_system_score_gemma":0.0009445988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001365743,"about_ca_topic_score_gemma":0.001874154,"domain_scores_codex":[0.9974299,0.0010805,0.0001529448,0.0004771379,0.0007180125,0.000141427],"domain_scores_gemma":[0.9972101,0.001521037,0.0004762186,0.0002677119,0.0003981022,0.0001268091],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004165865,0.0002225967,0.008367579,0.0003714139,0.0007628634,0.0005879054,0.0004754433,0.6791292,0.01897564,0.05124645,0.0009327946,0.2385114],"study_design_scores_gemma":[0.00001650306,0.0001007148,0.0004702723,0.00001019328,0.00008672701,0.00006332286,0.00003930089,0.9860581,0.002313882,0.009810519,0.001013823,0.00001660596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01773013,0.0003392296,0.9807137,0.0001952231,0.00002127578,0.00003980013,0.00002283695,0.0001584701,0.000779461],"genre_scores_gemma":[0.4481933,0.0003613713,0.5496056,0.0001474534,0.00006474879,0.0001319419,0.0001179902,0.00004503684,0.001332631],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003672507,"threshold_uncertainty_score":0.01942235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1596503619878044,"score_gpt":0.336622064281259,"score_spread":0.1769717022934547,"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."}}