{"id":"W4403792103","doi":"10.1002/smll.202406475","title":"Profiling Breast Tumor Heterogeneity and Identifying Breast Cancer Subtypes Through Tumor‐Associated Immune Cell Signatures and Immuno Nano Sensors","year":2024,"lang":"en","type":"article","venue":"Small","topic":"Immunotherapy and Immune Responses","field":"Immunology and Microbiology","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Toronto Metropolitan University; St. Michael's Hospital; Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Breast cancer; Immune system; Profiling (computer programming); Breast tumor; Medicine; Cancer; Cancer research; Oncology; Internal medicine; Immunology; Computer science","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":[0.000259942,0.000250823,0.0002610594,0.0005981948,0.0001169257,0.0004138353,0.0002218439,0.0004554064,0.0005916241],"category_scores_gemma":[0.0003173027,0.0001454843,0.0001915617,0.0002475808,0.0002114291,0.0003401758,0.0003011772,0.0003175076,0.000222017],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000231444,"about_ca_system_score_gemma":0.0001378409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002283908,"about_ca_topic_score_gemma":0.0006483062,"domain_scores_codex":[0.9997897,0.00004158615,0.000008452792,0.00005886378,0.00007146829,0.00002994398],"domain_scores_gemma":[0.9998732,0.00004534822,0.0000360766,0.00001065228,0.00002447891,0.00001031297],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001016844,0.00004320726,0.005406986,0.00006833426,0.00001871578,0.0000556696,0.0000331659,0.0009381259,0.9727015,0.0003601891,0.0001860489,0.02008632],"study_design_scores_gemma":[0.00001547332,0.0002756825,0.0181746,0.00002096575,0.0000572778,0.0005690774,0.0001691814,0.04693006,0.929285,0.000968628,0.003499203,0.00003478937],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8870934,0.00327274,0.1053475,0.0003247837,0.00007601523,0.00006900208,0.000449946,0.0003313427,0.00303522],"genre_scores_gemma":[0.9564319,0.000814533,0.04088312,0.0002569763,0.00003072582,0.00007396725,0.0001806816,0.0000226965,0.001305405],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005981948,"threshold_uncertainty_score":0.001979232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01306687768131983,"score_gpt":0.2495977242977302,"score_spread":0.2365308466164104,"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."}}