{"id":"W2144369203","doi":"10.1021/pr5013009","title":"Quest for Missing Proteins: Update 2015 on Chromosome-Centric Human Proteome Project","year":2015,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Genome British Columbia; University of Victoria","funders":"National Institute of Environmental Health Sciences; Seventh Framework Programme; Biotechnology and Biological Sciences Research Council; Ministry of Health and Welfare; Cancer Prevention and Research Institute of Texas; University of Texas Medical Branch; National Institutes of Health; Cancer Research Institute","keywords":"Human proteome project; Proteome; Computational biology; Identification (biology); Data science; Computer science; Biology; Bioinformatics; Proteomics; Genetics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004036503,0.0002879156,0.0004899757,0.0006464995,0.0004869238,0.0002313891,0.0009673097,0.0002621265,0.000101888],"category_scores_gemma":[0.001088825,0.0002386744,0.0002067955,0.0007623111,0.0002090815,0.0003970441,0.0001905504,0.001577985,0.00003497866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009098955,"about_ca_system_score_gemma":0.001178176,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003681024,"about_ca_topic_score_gemma":0.000002940139,"domain_scores_codex":[0.9961898,0.0001488653,0.0009289877,0.0004519795,0.001424933,0.0008554149],"domain_scores_gemma":[0.9959013,0.0001271088,0.0007054933,0.0006510152,0.0021679,0.0004471418],"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":[0.0008651873,0.0009373458,0.00009537757,0.0007247114,0.00008182102,0.00006695106,0.0002489797,0.00003305994,0.9689245,0.003165429,0.02045614,0.004400556],"study_design_scores_gemma":[0.002350264,0.001582508,0.000007650565,0.0008102127,0.00001945564,0.0001380872,0.0001721263,0.0002076972,0.7715617,0.03490702,0.1878728,0.0003705183],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.753508,0.00363202,0.1293058,0.03358804,0.0003716873,0.04782696,0.0005805623,0.0008584226,0.03032853],"genre_scores_gemma":[0.4953915,0.0002812243,0.4802874,0.0001261169,0.004485253,0.01156533,0.00009485497,0.0003830051,0.007385391],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3509816,"threshold_uncertainty_score":0.973286,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1597905780612112,"score_gpt":0.4670914908109767,"score_spread":0.3073009127497656,"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."}}