{"id":"W4212968246","doi":"10.1038/s41467-022-28524-0","title":"Proteomic analysis of archival breast cancer clinical specimens identifies biological subtypes with distinct survival outcomes","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Centre for Applied Research in Cancer Control; Vancouver General Hospital; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"Canadian Cancer Society Research Institute; Canadian Institutes of Health Research; Government of Canada","keywords":"Breast cancer; Immune system; Proteomics; Oncology; Medicine; Triple-negative breast cancer; Cancer; Bioinformatics; Biology; Computational biology; Internal medicine; Immunology; Gene; Genetics","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":[0.0003973008,0.0002265542,0.0002346046,0.001505378,0.0003650606,0.0004868715,0.0001585763,0.0002322909,0.0008108671],"category_scores_gemma":[0.000778977,0.00009280362,0.0002165218,0.001089839,0.0002518727,0.0001746122,0.0004574888,0.0001904694,0.000488443],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002136145,"about_ca_system_score_gemma":0.0002658032,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001377205,"about_ca_topic_score_gemma":0.003143712,"domain_scores_codex":[0.9997078,0.00003239279,0.00004328389,0.00008666,0.00008161064,0.00004823287],"domain_scores_gemma":[0.9997273,0.00004908542,0.0000871536,0.0000425739,0.00006147737,0.00003236391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001009068,0.00009455505,0.5622724,0.0003100406,0.0002365073,0.0007451068,0.0006725784,0.0002521665,0.384619,0.000197961,0.001878936,0.04771181],"study_design_scores_gemma":[0.000008613433,0.00009405361,0.9713866,0.000016468,0.00006121925,0.001672535,0.000432803,0.0002798432,0.02273205,0.0001766894,0.003131266,0.00000785766],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9938269,0.001210614,0.001130208,0.00008744883,0.00001459873,0.00003680324,0.002347062,0.00003147659,0.001314856],"genre_scores_gemma":[0.988511,0.001316524,0.003108111,0.0001230748,0.00002274674,0.00004895481,0.005886132,0.00001674315,0.0009667425],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001505378,"threshold_uncertainty_score":0.002738416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03892849364393146,"score_gpt":0.3722023467406029,"score_spread":0.3332738530966714,"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."}}