{"id":"W4306146231","doi":"10.1002/pmic.202200021","title":"Data‐independent acquisition and quantification of extracellular matrix from human lung in chronic inflammation‐associated carcinomas","year":2022,"lang":"en","type":"article","venue":"PROTEOMICS","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; McGill University Health Centre; Montreal General Hospital","funders":"NIH Office of the Director; University of California, San Francisco; Cancer Research UK; National Institutes of Health; McGill University Health Centre; Buck Institute for Research on Aging","keywords":"Extracellular matrix; Biomarker; Stromal cell; Inflammation; Proteomics; Biology; Pathology; Immunohistochemistry; Basement membrane; Cancer; Lung cancer; Stroma; Proteome; Quantitative proteomics; Biomarker discovery; Elastin; Cancer research; Cell biology; Immunology; Medicine; Bioinformatics; Biochemistry; Gene","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":[0.0003273692,0.0001132005,0.0001649502,0.00006791332,0.0001939426,0.0000216713,0.0003619215,0.00008372332,0.0002252013],"category_scores_gemma":[0.0000199681,0.000144548,0.00002432555,0.0001356269,0.00003793991,0.0001517236,0.0003677539,0.0002856182,0.000001090003],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005362275,"about_ca_system_score_gemma":0.0000717408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003675238,"about_ca_topic_score_gemma":0.00008644408,"domain_scores_codex":[0.9988204,0.00003472936,0.0004520421,0.0003630225,0.0001841978,0.0001455591],"domain_scores_gemma":[0.9988801,0.00003127399,0.0004012711,0.0006190853,0.00003837181,0.00002982412],"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":[0.00001689204,0.00004848605,0.005911861,0.00005536949,0.00001213691,0.000001349071,0.0001119408,0.001190686,0.9885781,0.003804659,0.00001541265,0.0002530806],"study_design_scores_gemma":[0.0008891245,0.00002686886,0.003414866,0.00004927254,0.00003714879,0.000003076112,0.0001705945,0.07921465,0.9015411,0.01376092,0.0006142112,0.0002782137],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9679028,0.000898462,0.02948882,0.0001096595,0.00001069372,0.0005767744,0.0008758155,0.00006787605,0.00006911322],"genre_scores_gemma":[0.9833271,0.00005572268,0.01253298,0.000002261175,0.00004796558,0.0005329018,0.003325687,0.00002386641,0.0001515119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08703707,"threshold_uncertainty_score":0.5894495,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02493314620037741,"score_gpt":0.2997027472567341,"score_spread":0.2747696010563567,"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."}}