{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001046255,0.0008922483,0.0005570936,0.002084447,0.0006804736,0.001033678,0.000566984,0.0005357019,0.001770819],"category_scores_gemma":[0.001375545,0.0003292428,0.0006140364,0.001326745,0.0003277842,0.0005467318,0.0009374713,0.0006329363,0.001069665],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000400097,"about_ca_system_score_gemma":0.0007723995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005343902,"about_ca_topic_score_gemma":0.001366245,"domain_scores_codex":[0.999225,0.0001384322,0.00005230241,0.0001767337,0.0003222155,0.00008532945],"domain_scores_gemma":[0.9994226,0.0001565422,0.00008431316,0.00006924011,0.0001845591,0.00008280999],"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":[0.0001545033,0.0001000242,0.0007505809,0.000123639,0.00004100425,0.00006169601,0.00004760012,0.0003850101,0.9826781,0.0002683405,0.0004181536,0.01497135],"study_design_scores_gemma":[0.0000186146,0.0002159931,0.003713125,0.00002571959,0.00004993321,0.0004402194,0.00004885704,0.004588658,0.984441,0.0003502717,0.006068031,0.00003967423],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5347257,0.004508758,0.4417031,0.0004860139,0.000163879,0.001006946,0.007262156,0.005484628,0.004658745],"genre_scores_gemma":[0.536772,0.003171373,0.4419179,0.00101643,0.0000831597,0.001038762,0.009891436,0.0007135375,0.005395346],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002084447,"threshold_uncertainty_score":0.005923986,"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."}}