{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004360326,0.000427673,0.0005064233,0.0001487275,0.0005057584,0.0001918217,0.0002257914,0.0002699791,0.0002110575],"category_scores_gemma":[0.00002951278,0.0003688596,0.000162642,0.0002445358,0.0003911012,0.0002859718,0.0001911818,0.0006300135,0.00007282275],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008339256,"about_ca_system_score_gemma":0.00013765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001110835,"about_ca_topic_score_gemma":0.0000880525,"domain_scores_codex":[0.9977991,0.0004228653,0.0004619011,0.0006081549,0.00006003557,0.0006479642],"domain_scores_gemma":[0.9991717,0.0002436499,0.0001574574,0.0003045237,0.00009561778,0.0000270422],"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":[0.0006980881,0.00009260357,0.004270465,0.0002354304,0.0005715434,0.00005443085,0.0009021402,0.000005968192,0.9913946,0.000135813,0.0000932569,0.001545626],"study_design_scores_gemma":[0.001777385,0.0001503529,0.1092325,0.0007605378,0.0002169748,0.001979281,0.0007278459,0.00003397001,0.8810142,0.0001158113,0.003266705,0.0007244395],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7918302,0.2060993,0.00001136626,0.0001846628,0.0009662767,0.0002872989,0.0003068852,0.0001999296,0.0001141097],"genre_scores_gemma":[0.9946173,0.002991813,0.00006388984,0.0001651933,0.00004904677,0.00004509275,0.00007913139,0.00007291714,0.001915554],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2031075,"threshold_uncertainty_score":0.9998763,"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."}}