{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007950144,0.003501517,0.002856818,0.007828012,0.001428574,0.008500393,0.005274659,0.002560704,0.02739554],"category_scores_gemma":[0.01331768,0.001705606,0.001607218,0.00745562,0.0007571962,0.00851718,0.005892138,0.004725316,0.04394964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001946875,"about_ca_system_score_gemma":0.005682212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009311943,"about_ca_topic_score_gemma":0.01060391,"domain_scores_codex":[0.995372,0.0006251683,0.0006523647,0.0007204294,0.002212896,0.0004172344],"domain_scores_gemma":[0.987106,0.001850616,0.001074863,0.002452877,0.005081922,0.00243377],"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":[0.0005600487,0.0001393067,0.001923999,0.001960256,0.0001510443,0.0002316985,0.0001843027,0.0002839534,0.01501673,0.002924748,0.8243312,0.1522927],"study_design_scores_gemma":[0.00004431356,0.0000318433,0.001453143,0.0003581117,0.00007277184,0.0002881142,0.00003862267,0.0005944851,0.004771864,0.001773429,0.990478,0.00009521191],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01189246,0.05904022,0.143915,0.01858684,0.006675959,0.0008909031,0.3766452,0.3311648,0.05118865],"genre_scores_gemma":[0.01331589,0.02142592,0.1331134,0.01127294,0.001837379,0.0008166827,0.7543404,0.02984259,0.03403476],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.02739554,"threshold_uncertainty_score":0.09164721,"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."}}