{"id":"W1980136706","doi":"10.1371/journal.pcbi.1002963","title":"Computational Biomarker Pipeline from Discovery to Clinical Implementation: Plasma Proteomic Biomarkers for Cardiac Transplantation","year":2013,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vancouver General Hospital; Institute of Infection and Immunity; University of Victoria; Prevention of Organ Failure; University of British Columbia","funders":"Novartis Pharma; Astellas Pharma; Genome British Columbia; St. Paul's Foundation; University of Victoria; Genome Canada","keywords":"Biomarker discovery; Biomarker; Multiplex; Proteomics; Computational biology; Transplantation; Proteome; Quantitative proteomics; Bioinformatics; Computer science; Medicine; Biology; Internal medicine","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":[0.0001448438,0.0002492372,0.0003416463,0.00009971233,0.0001913914,0.00007235754,0.000247681,0.0001853172,0.0005062346],"category_scores_gemma":[0.00004512972,0.0002491388,0.0001935146,0.0001496961,0.0001182532,0.0002218633,0.00005682927,0.0001335669,0.0001140247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009055492,"about_ca_system_score_gemma":0.000118265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001183336,"about_ca_topic_score_gemma":0.000006523344,"domain_scores_codex":[0.9980062,0.00006040473,0.0008175781,0.0006512555,0.0001590503,0.0003055568],"domain_scores_gemma":[0.99817,0.0009419399,0.0002475269,0.0001982456,0.0002971375,0.000145176],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00216629,0.001637409,0.1491588,0.000442474,0.002719875,0.000004975067,0.0006934041,0.03161138,0.5133485,0.04825229,0.03217627,0.2177883],"study_design_scores_gemma":[0.007205777,0.0003873423,0.04990222,0.0001440405,0.0003743209,0.00001526538,0.0003786619,0.360586,0.09366966,0.4757437,0.009611683,0.001981326],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.507087,0.00001512363,0.4871713,0.001406202,0.00008454468,0.001166384,0.002813271,0.0001227987,0.0001333533],"genre_scores_gemma":[0.6396238,0.00001553343,0.3439859,0.0003803309,0.0002784845,0.002447864,0.01317701,0.00003390989,0.00005715287],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4274914,"threshold_uncertainty_score":0.9999961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03375821834990445,"score_gpt":0.3506546587721947,"score_spread":0.3168964404222903,"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."}}