{"id":"W2759592955","doi":"10.1021/acs.jproteome.7b00467","title":"The Human Plasma Proteome Draft of 2017: Building on the Human Plasma PeptideAtlas from Mass Spectrometry and Complementary Assays","year":2017,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":243,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Environmental Health Sciences; National Cancer Institute; National Institute of General Medical Sciences; Knut och Alice Wallenbergs Stiftelse","keywords":"Proteome; Human plasma; Human proteome project; Proteomics; Computational biology; Mass spectrometry; Blood proteins; Bioinformatics; Biology; Chemistry; Biochemistry; Chromatography; Gene","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":[0.01884069,0.003434587,0.002177121,0.0054135,0.001992654,0.006361328,0.003273822,0.002803431,0.0190416],"category_scores_gemma":[0.03405326,0.002716889,0.002981265,0.004932129,0.001263222,0.004250702,0.005564915,0.004744529,0.03779111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001919811,"about_ca_system_score_gemma":0.01420125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006586305,"about_ca_topic_score_gemma":0.007570661,"domain_scores_codex":[0.9938161,0.001251662,0.000942255,0.0007536017,0.00278774,0.0004486354],"domain_scores_gemma":[0.9820347,0.003954485,0.001645888,0.002799423,0.008449164,0.001116387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00179177,0.0001652232,0.005363196,0.004655465,0.0003617607,0.001563888,0.001070971,0.001780399,0.04028525,0.01708052,0.830248,0.09563363],"study_design_scores_gemma":[0.0001773087,0.0001344768,0.004928355,0.00106904,0.0001401669,0.0006903678,0.0002005997,0.001729737,0.01701524,0.008351324,0.9654168,0.000146556],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01235405,0.009004232,0.3695562,0.01169246,0.005627578,0.002183269,0.4941155,0.06977937,0.02568742],"genre_scores_gemma":[0.008634225,0.004540883,0.3656021,0.002882497,0.0007160355,0.002439181,0.5990061,0.0100798,0.006099151],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0190416,"threshold_uncertainty_score":0.09964031,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.100479405335578,"score_gpt":0.4165295949624361,"score_spread":0.316050189626858,"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."}}