{"id":"W4375866628","doi":"10.18280/rces.100101","title":"Classification of Breast Cancer Using Ensemble Empirical Mode Decomposition and Autoencoder-Based Methods","year":2023,"lang":"en","type":"article","venue":"Review of Computer Engineering Studies","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Autoencoder; Breast cancer; Hilbert–Huang transform; Computer science; Artificial intelligence; Cancer; Pattern recognition (psychology); Decomposition; Mathematics; Medicine; Statistics; Internal medicine; Deep learning; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001151237,0.000708601,0.001024503,0.001239393,0.0001403038,0.0005512488,0.0005489031,0.0004838961,0.0005695052],"category_scores_gemma":[0.001350831,0.0001834845,0.001062706,0.0007901859,0.0001318409,0.0006415439,0.0004086114,0.000705887,0.000360683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001754144,"about_ca_system_score_gemma":0.0002819464,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001355681,"about_ca_topic_score_gemma":0.001750054,"domain_scores_codex":[0.9996165,0.00009318959,0.00003216384,0.00009055964,0.0001303084,0.00003713315],"domain_scores_gemma":[0.9995348,0.0001703167,0.00003820601,0.00004431265,0.0001964108,0.00001588877],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002214775,0.0001915314,0.008789827,0.0002535318,0.0003027701,0.00008545561,0.00007721795,0.07882903,0.02144841,0.001182027,0.003241965,0.8853768],"study_design_scores_gemma":[0.00001256216,0.0001172698,0.00814824,0.00004811759,0.0001326384,0.0001818167,0.00004807777,0.9828036,0.00519643,0.001883661,0.001403702,0.00002377055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1157841,0.0080311,0.8731394,0.0003256591,0.0001738304,0.00007314802,0.000387759,0.0006786368,0.001406408],"genre_scores_gemma":[0.6488121,0.006718145,0.3387199,0.0001990686,0.0003373356,0.0001484552,0.001870153,0.0001021143,0.003092727],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001355681,"threshold_uncertainty_score":0.006088376,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04537139729789715,"score_gpt":0.4636490416228196,"score_spread":0.4182776443249224,"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."}}