{"id":"W1522023542","doi":"10.1109/ijcnn.2005.1556291","title":"Virtual reality visual data mining with nonlinear discriminant neural networks: application to leukemia and Alzheimer gene expression data","year":2006,"lang":"en","type":"article","venue":"Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Artificial intelligence; Artificial neural network; Data mining; Generalization; Machine learning; Pattern recognition (psychology); Mathematics","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":[0.001703616,0.0004511694,0.0006579164,0.001081275,0.0003290388,0.0009109805,0.0006724159,0.0006478766,0.0006765524],"category_scores_gemma":[0.005250992,0.0002750863,0.0006614925,0.001017723,0.0004919673,0.0005486225,0.0009663298,0.0006109372,0.0001472832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005871488,"about_ca_system_score_gemma":0.0004604856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003378301,"about_ca_topic_score_gemma":0.003333065,"domain_scores_codex":[0.9994874,0.0002687182,0.00002506252,0.00007448257,0.0001053979,0.00003891645],"domain_scores_gemma":[0.9984295,0.001106722,0.0001066441,0.0001266943,0.0001808218,0.00004966378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004613085,0.0002305103,0.00355689,0.0001397305,0.000116567,0.0002933142,0.0002627163,0.5641083,0.008222552,0.005343556,0.002099134,0.4151655],"study_design_scores_gemma":[0.000009649242,0.00002144227,0.0003553643,0.000003103633,0.000004207835,0.00003453523,0.00001951961,0.9961609,0.001204782,0.00191569,0.0002649791,0.000005958893],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.159207,0.0007321669,0.8366855,0.0009356835,0.00007332713,0.00008442243,0.000177657,0.0008366977,0.001267537],"genre_scores_gemma":[0.6643944,0.0002993725,0.3338545,0.0001120901,0.00004223103,0.00008469254,0.0001808184,0.00004864829,0.0009832169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003378301,"threshold_uncertainty_score":0.009009719,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08209635693344464,"score_gpt":0.3194906335331427,"score_spread":0.2373942765996981,"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."}}