{"id":"W4393969605","doi":"10.1021/acs.jproteome.4c00009","title":"Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine","year":2024,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Institute of Cancer Research; NIH Clinical Center; National Cancer Institute; National Institutes of Health; University of Toronto; Canadian Prostate Cancer Research Initiative; Prostate Cancer Canada; Eastern Virginia Medical School; Canadian Institutes of Health Research; Sunnybrook Research Institute","keywords":"Biomarker discovery; Urine; Prostate cancer; Biomarker; Proteome; Prostate; Computational biology; Computer science; Cancer; Chemistry; Proteomics; Bioinformatics; Medicine; Internal medicine; Biology; 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":[0.0006849787,0.0001366339,0.0002326243,0.0004323319,0.0001287757,0.0001713527,0.0002807606,0.0001157688,0.00008893755],"category_scores_gemma":[0.0001710547,0.000117697,0.0001284941,0.000573723,0.0001310819,0.0003203255,0.00005730039,0.001001411,0.000006608756],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002736538,"about_ca_system_score_gemma":0.0004077702,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001202285,"about_ca_topic_score_gemma":0.000004763872,"domain_scores_codex":[0.9983516,0.00004983729,0.0005117366,0.0002474869,0.0004279004,0.0004114294],"domain_scores_gemma":[0.9988969,0.0002110509,0.0001206721,0.000207589,0.0004630327,0.0001007861],"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":[0.0005465543,0.00008199668,0.000007465342,0.0009680798,0.00001739104,0.00002669212,0.00009344658,0.0001086669,0.989795,0.0009148245,0.0000568863,0.007382999],"study_design_scores_gemma":[0.0005582375,0.0003597276,0.00001497343,0.0006013265,0.000003854367,0.00001786818,0.00008195405,0.00259341,0.9453467,0.03951873,0.01078856,0.0001146385],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8505415,0.0008786301,0.1439135,0.00176742,0.00003381821,0.002279313,0.00003246836,0.00009455589,0.000458816],"genre_scores_gemma":[0.9173535,0.00006762285,0.07979652,0.000007497969,0.0003064595,0.001632825,0.000005563087,0.00004523557,0.0007848002],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.066812,"threshold_uncertainty_score":0.4799542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04655095450357444,"score_gpt":0.375923448846514,"score_spread":0.3293724943429395,"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."}}