{"id":"W2269102739","doi":"10.1373/clinchem.2015.247858","title":"Proteogenomics: Opportunities and Caveats","year":2016,"lang":"en","type":"article","venue":"Clinical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Mount Sinai Hospital; University of Toronto","funders":"Prostate Cancer Canada","keywords":"Proteogenomics; Computational biology; Biology; Proteome; Proteomics; Ensembl; Genomics; Pseudogene; Genome; Identification (biology); Druggability; Human proteome project; Exome; Exome sequencing; DNA sequencing; Genetics; Gene; Phenotype","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009792022,0.0001238668,0.0001709487,0.000004482475,0.00006447403,0.0000148522,0.0001745508,0.0001947276,0.0006456118],"category_scores_gemma":[0.0001361662,0.00009132595,0.00006894685,0.00001692373,0.0002952905,0.00004716952,0.0001250991,0.0001492183,0.0000118811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002635359,"about_ca_system_score_gemma":0.00004237295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.87633e-7,"about_ca_topic_score_gemma":1.337226e-7,"domain_scores_codex":[0.9990763,0.000004585378,0.0003604443,0.0003232285,0.00006211118,0.0001733283],"domain_scores_gemma":[0.9990968,0.0001680334,0.0001192114,0.0004043872,0.00004023384,0.0001713394],"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":[0.00006405864,0.0001496929,0.02691153,0.00021463,0.00004615052,0.00001785892,0.00001349767,8.88022e-8,0.7735572,0.002965209,0.002006244,0.1940538],"study_design_scores_gemma":[0.0004306357,0.000009257034,0.00005825245,0.00008059119,0.00001317381,0.00002089616,0.00003420218,0.000007972825,0.8174713,0.009539034,0.172103,0.0002316762],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9603819,0.0001527523,0.001602071,0.002175655,0.00001352892,0.00009439143,0.00007674043,0.0002324044,0.03527055],"genre_scores_gemma":[0.9627704,0.002874597,0.009493321,0.0001880863,0.000467223,0.0002347594,0.00001660862,0.00004034494,0.02391468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1938221,"threshold_uncertainty_score":0.7068996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08803035209775073,"score_gpt":0.3526974424539431,"score_spread":0.2646670903561924,"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."}}