{"id":"W2506055767","doi":"10.1021/acs.jproteome.6b00511","title":"Metrics for the Human Proteome Project 2016: Progress on Identifying and Characterizing the Human Proteome, Including Post-Translational Modifications","year":2016,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":77,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Manitoba","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Environmental Health Sciences; National Institute of General Medical Sciences; Seventh Framework Programme; Knut och Alice Wallenbergs Stiftelse","keywords":"Human proteome project; Proteome; Computational biology; Posttranslational modification; Human proteins; Proteomics; Data science; Computer science; Biology; Bioinformatics; Chemistry; Biochemistry; Enzyme","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":[0.02380278,0.002542505,0.001996904,0.007100549,0.001375844,0.006628577,0.002059424,0.00118835,0.007613716],"category_scores_gemma":[0.04378188,0.0006967172,0.001401299,0.01108785,0.000563641,0.00446025,0.007116986,0.002192728,0.007320488],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003475539,"about_ca_system_score_gemma":0.008908436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009201802,"about_ca_topic_score_gemma":0.007348403,"domain_scores_codex":[0.982156,0.005479321,0.002431062,0.001876226,0.007413644,0.0006437948],"domain_scores_gemma":[0.9828959,0.002710559,0.002846249,0.001649898,0.008416177,0.001481171],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001113894,0.0001916577,0.02617711,0.008150719,0.0008453236,0.0001467054,0.0007825492,0.002037608,0.006910542,0.02910437,0.6933396,0.2311998],"study_design_scores_gemma":[0.0001975748,0.0003855452,0.05437811,0.002360782,0.0003175878,0.0003598052,0.0004944148,0.008120806,0.01072179,0.02079975,0.9016612,0.0002025256],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"review","genre_scores_codex":[0.05021796,0.04756062,0.1421749,0.01513154,0.004004511,0.003660885,0.6516566,0.0336498,0.05194312],"genre_scores_gemma":[0.04729744,0.01067314,0.229906,0.001863383,0.0003988804,0.003971437,0.6951944,0.003659954,0.00703545],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02380278,"threshold_uncertainty_score":0.1258826,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2243731098460864,"score_gpt":0.4711819682970833,"score_spread":0.2468088584509969,"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."}}