{"id":"W2090166572","doi":"10.1002/prca.200800019","title":"A qualitative proteome investigation of the sediment portion of human urine: Implications in the biomarker discovery process","year":2008,"lang":"en","type":"article","venue":"PROTEOMICS - CLINICAL APPLICATIONS","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Izaak Walton Killam Health Centre; Institute for Marine Biosciences; Dalhousie University","funders":"Natural Sciences and Engineering Research Council of Canada; Dalhousie University","keywords":"Biomarker discovery; Proteome; Biomarker; Urine; Computational biology; Process (computing); Sediment; Human proteome project; Biology; Proteomics; Bioinformatics; Medicine; Computer science; Internal medicine; Genetics; Gene; Paleontology","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":[0.0009447859,0.0004911309,0.0003044983,0.0009812906,0.000483444,0.0005985727,0.0001833811,0.0004116995,0.0007048898],"category_scores_gemma":[0.001294557,0.0001664065,0.0002195735,0.0007718796,0.0004420545,0.0003742279,0.0002989783,0.0003849247,0.0002432264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001599173,"about_ca_system_score_gemma":0.0003812223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005244776,"about_ca_topic_score_gemma":0.0008677945,"domain_scores_codex":[0.9996529,0.0001239344,0.00003612042,0.00005376207,0.0001071224,0.00002615426],"domain_scores_gemma":[0.9994754,0.0001980524,0.00009258447,0.00003959926,0.0001440354,0.00005036809],"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":[0.0001608535,0.00003071015,0.003648096,0.0001185632,0.00001444764,0.0001210029,0.0000698986,0.00008862949,0.9894751,0.000129697,0.00005486189,0.006088112],"study_design_scores_gemma":[0.00001337122,0.0007429477,0.05442312,0.00004027917,0.00007911365,0.001520629,0.0003340532,0.001625196,0.9381282,0.0006276788,0.002435309,0.0000301592],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9424268,0.00310846,0.05152811,0.0005305598,0.00008191709,0.0001356269,0.0007386096,0.0001585046,0.001291437],"genre_scores_gemma":[0.9216266,0.002401705,0.07358181,0.0002939559,0.00004156442,0.00009405481,0.0005537915,0.00003764983,0.001368876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009812906,"threshold_uncertainty_score":0.004996598,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1060451235873162,"score_gpt":0.4403117094601121,"score_spread":0.3342665858727958,"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."}}