{"id":"W2039989552","doi":"10.1021/pr0705439","title":"Protein Identification and Peptide Expression Resolver: Harmonizing Protein Identification with Protein Expression Data","year":2007,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Caprion (Canada)","funders":"U.S. Public Health Service","keywords":"Identification (biology); Protein expression; Peptide; Resolver; Biomarker discovery; Proteomics; Bottom-up proteomics; Computational biology; Biology; Biochemistry; Chemistry; Computer science; Tandem mass spectrometry; Gene; Protein mass spectrometry; Mass spectrometry","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":[0.01143934,0.001076122,0.001541831,0.002123125,0.000533558,0.001722269,0.001435938,0.001042543,0.001556803],"category_scores_gemma":[0.01478128,0.0007572821,0.0005084196,0.001746644,0.0007447231,0.002483024,0.001641861,0.001484584,0.001969498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002567226,"about_ca_system_score_gemma":0.0006612297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001589052,"about_ca_topic_score_gemma":0.0002071498,"domain_scores_codex":[0.9942757,0.001652285,0.0005267343,0.001300619,0.002045237,0.0001994282],"domain_scores_gemma":[0.9945087,0.002225985,0.001271801,0.001128329,0.0007223586,0.0001427568],"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":[0.001196545,0.000299716,0.00622867,0.0005984548,0.0001425457,0.0008518,0.0002945571,0.001889521,0.8018457,0.001744784,0.002152243,0.1827554],"study_design_scores_gemma":[0.000105822,0.0008029506,0.01384602,0.0000483465,0.00011247,0.005160869,0.000100016,0.06953786,0.8948449,0.002121024,0.01316374,0.0001560894],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1542367,0.0009780643,0.8320439,0.0005240786,0.0001053095,0.0003754461,0.0009781217,0.009258695,0.001499689],"genre_scores_gemma":[0.1322128,0.0005069639,0.8631371,0.0002662203,0.00006109745,0.0003172719,0.001559465,0.0005533031,0.001385808],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01143934,"threshold_uncertainty_score":0.0604977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1133103649860695,"score_gpt":0.3881823402315962,"score_spread":0.2748719752455267,"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."}}