{"id":"W3135806161","doi":"10.1021/acs.jproteome.0c00961","title":"An Update on MRMAssayDB: A Comprehensive Resource for Targeted Proteomics Assays in the Community","year":2021,"lang":"en","type":"article","venue":"Journal of Proteome Research","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital; University of Victoria","funders":"Warren Y. Soper Charitable Trust; Fondation De Famille Alvin Segal; Ministry of Science and Higher Education of the Russian Federation; Jewish General Hospital; Terry Fox Research Institute; European Regional Development Fund; Fundacja na rzecz Nauki Polskiej; European Commission; Genome British Columbia; Skolkovo Institute of Science and Technology; Genome Canada; McGill University","keywords":"Proteomics; Computational biology; Computer science; Annotation; Quantitative proteomics; Resource (disambiguation); Bioinformatics; Biology; Gene; Genetics","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":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.004195995,0.0001547985,0.0003005435,0.0001910053,0.0005673807,0.0001395585,0.001163601,0.0001702244,0.00005943501],"category_scores_gemma":[0.0007021247,0.000116262,0.0001398179,0.0005658693,0.0001613057,0.0001635795,0.0001437259,0.003304867,0.000004597966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002308597,"about_ca_system_score_gemma":0.0003095648,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002260614,"about_ca_topic_score_gemma":0.00001465554,"domain_scores_codex":[0.9971086,0.0009328758,0.0005763859,0.0002016296,0.000723912,0.0004566172],"domain_scores_gemma":[0.9965611,0.0009099939,0.0003022732,0.0008965231,0.001202773,0.0001273336],"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":[0.0005154703,0.0008467421,0.0001085405,0.0001848222,0.00002975238,0.00005319799,0.0006872631,0.0002546397,0.9899782,0.00410364,0.001526659,0.001711093],"study_design_scores_gemma":[0.001251599,0.0005908529,0.0001701295,0.0002491999,0.000009946459,0.0001453392,0.00399065,0.0007186304,0.8839447,0.04768629,0.06104933,0.0001933283],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9681259,0.0002097431,0.01974524,0.007390032,0.00001592884,0.001829421,0.0001028578,0.0000351233,0.002545723],"genre_scores_gemma":[0.8440019,0.000150124,0.1537154,0.0003558334,0.0003417479,0.001115655,0.00006973805,0.00005977312,0.0001897461],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1339702,"threshold_uncertainty_score":0.9989945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1203083969924905,"score_gpt":0.432516309391677,"score_spread":0.3122079123991864,"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."}}