{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001030831,0.0006484808,0.0005034361,0.001071699,0.0004435532,0.0006135213,0.0003850902,0.000449215,0.001127871],"category_scores_gemma":[0.00143444,0.0003050841,0.0002914428,0.001006391,0.0003330761,0.0003542892,0.0007553577,0.0004989148,0.0006072721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002980565,"about_ca_system_score_gemma":0.0007511482,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001177881,"about_ca_topic_score_gemma":0.00207659,"domain_scores_codex":[0.9993649,0.0000724522,0.00008067841,0.0002042199,0.0002180163,0.00005981404],"domain_scores_gemma":[0.9993118,0.0001824564,0.00007353102,0.0001263062,0.0002580361,0.00004796222],"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":[0.0004063332,0.00005348249,0.003474189,0.0001447278,0.00003591885,0.00009270268,0.00006522996,0.0004554954,0.9860852,0.00009060326,0.0003207738,0.008775233],"study_design_scores_gemma":[0.00004465791,0.0002159988,0.05535562,0.00001435791,0.00006648845,0.0005578335,0.0000886974,0.01868982,0.9209396,0.000336345,0.003650842,0.00003965417],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8707188,0.001010486,0.113231,0.0002023281,0.00005668047,0.0003788921,0.01111731,0.001865133,0.001419308],"genre_scores_gemma":[0.7791913,0.0009603378,0.1997241,0.0003066837,0.00003068541,0.001186578,0.0164463,0.0005423281,0.001611672],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001177881,"threshold_uncertainty_score":0.00545162,"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."}}