{"id":"W3093703323","doi":"10.1021/acs.jproteome.0c00502","title":"Proteomic Profiling of the Human Tissue and Biological Fluid Proteome","year":2020,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; Lunenfeld-Tanenbaum Research Institute; Mount Sinai Hospital","funders":"","keywords":"Proteome; Biomarker discovery; Human proteome project; Proteomics; Computational biology; Human genome; Tandem mass tag; Biology; Tandem mass spectrometry; Genome; Human Protein Atlas; Bioinformatics; Mass spectrometry; Gene; Chemistry; Quantitative proteomics; Genetics; Chromatography","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0004102658,0.000379796,0.0003820522,0.001216483,0.000268124,0.0003835864,0.0002145825,0.000240678,0.0004980896],"category_scores_gemma":[0.0004847095,0.00009037565,0.0002394849,0.0008727447,0.0001799163,0.0002067022,0.0003727805,0.0002113047,0.0003674746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001122308,"about_ca_system_score_gemma":0.0003023729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004787208,"about_ca_topic_score_gemma":0.0004458175,"domain_scores_codex":[0.9997163,0.00006019899,0.00002702261,0.00007312671,0.00008938284,0.0000338933],"domain_scores_gemma":[0.999879,0.00002867379,0.00001868361,0.00001382502,0.00004259169,0.00001726138],"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":[0.0001747255,0.00003301972,0.01099863,0.0001803396,0.00005161645,0.0003557538,0.00007832232,0.0001632742,0.9737802,0.00009504371,0.0002004495,0.01388861],"study_design_scores_gemma":[0.00002486868,0.0006970708,0.1305595,0.00006311329,0.0001919348,0.004895522,0.000380234,0.003969813,0.8448506,0.0005634436,0.01376126,0.00004262264],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9654164,0.008097088,0.02013695,0.0001687369,0.00004843074,0.0001222027,0.004299423,0.0001670226,0.001543748],"genre_scores_gemma":[0.94304,0.007207144,0.04152334,0.0001932818,0.00006716925,0.0001136135,0.006615464,0.00004850859,0.001191575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001216483,"threshold_uncertainty_score":0.002169669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1240802779797961,"score_gpt":0.4113416206054859,"score_spread":0.2872613426256898,"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."}}