{"id":"W2930120357","doi":"10.3390/cancers11040494","title":"Association Analysis of Deep Genomic Features Extracted by Denoising Autoencoders in Breast Cancer","year":2019,"lang":"en","type":"article","venue":"Cancers","topic":"AI in cancer detection","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"CancerCare Manitoba; Research Institute in Oncology and Hematology; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Manitoba Health Research Council","keywords":"Breast cancer; Artificial intelligence; Proportional hazards model; Data set; Deep learning; Test set; Pattern recognition (psychology); Computer science; Cancer; Computational biology; Medicine; Biology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001357443,0.0003840071,0.0006310997,0.0008819116,0.0001539768,0.0004095508,0.0003602978,0.0004122668,0.0004147682],"category_scores_gemma":[0.002983976,0.0002181489,0.0007043879,0.000614491,0.0002315195,0.0003025952,0.0004813959,0.0005827792,0.0001517759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004191533,"about_ca_system_score_gemma":0.0004833167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005343266,"about_ca_topic_score_gemma":0.005247056,"domain_scores_codex":[0.9996388,0.0001075884,0.0000203411,0.0001062935,0.00005835608,0.00006852284],"domain_scores_gemma":[0.9991475,0.0005058946,0.0001016433,0.00006270574,0.0001381146,0.00004414745],"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":[0.001479014,0.0005779571,0.3318295,0.0002695909,0.001002271,0.001025086,0.000462263,0.3460367,0.03522374,0.003640685,0.004704653,0.2737486],"study_design_scores_gemma":[0.0000172189,0.00008122335,0.05672501,0.00001731441,0.0001045264,0.0001146487,0.00006597935,0.9361778,0.003572275,0.002456819,0.0006442338,0.00002293887],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8964793,0.001031943,0.1003269,0.0004156221,0.00004153974,0.00002781446,0.0006911168,0.0003271678,0.0006585491],"genre_scores_gemma":[0.9853521,0.0002364656,0.01247615,0.00007314947,0.00001758882,0.0000273176,0.001118362,0.0000187632,0.0006801577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005343266,"threshold_uncertainty_score":0.01062429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004827881346927863,"score_gpt":0.2344452934275575,"score_spread":0.2296174120806296,"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."}}