{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002270295,0.0001175072,0.0002739207,0.0003635205,0.00003550824,0.00005188623,0.0003992258,0.0001108638,0.00009235202],"category_scores_gemma":[0.00001099995,0.0001290829,0.0000974401,0.001837716,0.00001832795,0.0003969079,0.00004904876,0.000170506,0.000005691002],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00774414,"about_ca_system_score_gemma":0.0003287654,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006979516,"about_ca_topic_score_gemma":0.004390725,"domain_scores_codex":[0.9987481,0.00006089771,0.0002461662,0.0003701101,0.0003063625,0.0002684074],"domain_scores_gemma":[0.9991376,0.00007495517,0.0003250172,0.0003186369,0.000096575,0.00004717283],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0000370086,0.00001274423,0.3550998,0.0000276176,0.0007676613,0.000001297362,0.001689342,0.5727045,0.03983001,0.0000637009,0.002998229,0.02676805],"study_design_scores_gemma":[0.0003834697,0.00001806457,0.6102896,0.0000271108,0.0001569272,0.000001146565,0.00009619055,0.3857928,0.00248692,0.00004661844,0.000492628,0.0002085109],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9564219,0.001146365,0.03949919,0.0008178406,0.001055365,0.0002294676,0.00006611204,0.00009456884,0.0006692079],"genre_scores_gemma":[0.9984687,0.0001562708,0.0008520983,0.000214004,0.00003277453,0.0000213444,0.000007633224,0.000009923404,0.0002372398],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2551897,"threshold_uncertainty_score":0.9996331,"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."}}