{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T13:00:24Z","timestamp":1790427624607,"version":"4.1.0"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T00:00:00Z","timestamp":1790380800000},"content-version":"vor","delay-in-days":25,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"Research Project of the National Institute of Metrology","award":["AKYZD2111"],"award-info":[{"award-number":["AKYZD2111"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["21927812"],"award-info":[{"award-number":["21927812"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Plan for Leading Talents of Science and Technology Innovation","award":["WR2202"],"award-info":[{"award-number":["WR2202"]}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["32503260"],"award-info":[{"award-number":["32503260"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2022YFF0608404"],"award-info":[{"award-number":["2022YFF0608404"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2022YFF0705001"],"award-info":[{"award-number":["2022YFF0705001"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2022FY101202"],"award-info":[{"award-number":["2022FY101202"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,9,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Ensuring robust quality control (QC) remains a major challenge in quantitative proteomics, particularly in detecting and managing outliers. Deep learning offers powerful representational capacity for ultra-high-dimensional data but often suffers from overfitting in small-sample scenarios. To address this, we propose memory-constrained outlier detection (MCOD), a deep anomaly detection framework that directly processes MaxQuant outputs and achieves competitive recall at high precision levels, which suggests a reduced risk of overlooking true outliers. MCOD integrates two innovations: (i) a memory-constrained module (MC module) that mitigates over-representation of samples via prototype-based regularization, and (ii) an adaptive steady-aware regulator that dynamically adjusts the per-sample loss weights in the MC module according to the estimated overfitting risk. Across two simulation settings based on a human cervical cancer cell line (HeLa) proteomics dataset and two real-world cancer proteomics datasets, MCOD consistently outperformed 18 statistical, machine learning, and deep learning baselines, achieving superior area under the receiver operating characteristic curve and area under the precision-recall curve scores. Functional enrichment analyses on the two real-world datasets showed that MCOD performed favorably compared to the three domain-specific models. Furthermore, feature-level visualization provided insights into the rationale behind the model\u2019s anomaly assignments. Collectively, MCOD establishes a robust and scalable framework for data QC in quantitative proteomics.<\/jats:p>","DOI":"10.1093\/bib\/bbag507","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T11:24:27Z","timestamp":1788780267000},"source":"Crossref","is-referenced-by-count":0,"title":["MCOD: a memory-constrained deep learning framework for robust outlier detection in quantitative proteomics"],"prefix":"10.1093","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9066-987X","authenticated-orcid":false,"given":"Jinze","family":"Huang","sequence":"first","affiliation":[{"name":"Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology , 18, Beisanhuandonglu, Chaoyang District, 100029, Beijing ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanyue","family":"Liao","sequence":"additional","affiliation":[{"name":"Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology , 18, Beisanhuandonglu, Chaoyang District, 100029, Beijing ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Meng","sequence":"additional","affiliation":[{"name":"Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology , 18, Beisanhuandonglu, Chaoyang District, 100029, Beijing ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangkui","family":"Fan","sequence":"additional","affiliation":[{"name":"Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology , 18, Beisanhuandonglu, Chaoyang District, 100029, Beijing ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dong","family":"An","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University , No. 17 Tsinghua East Road, Haidian District, Beijing 100083 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhua","family":"Dai","sequence":"additional","affiliation":[{"name":"China National Institute of Standardization , No. 9 Madian East Road, Haidian District, Beijing 100191 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Fang","sequence":"additional","affiliation":[{"name":"Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology , 18, Beisanhuandonglu, Chaoyang District, 100029, Beijing ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1444-5927","authenticated-orcid":false,"given":"Yang","family":"Zhao","sequence":"additional","affiliation":[{"name":"Technology Innovation Center of Mass Spectrometry for State Market Regulation, Center for Advanced Measurement Science, National Institute of Metrology , 18, Beisanhuandonglu, Chaoyang District, 100029, Beijing ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,26]]},"reference":[{"key":"2026092608242615300_ref1","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1021\/acs.jproteome.3c00102","article-title":"Evolution of mass spectrometry instruments and techniques for blood proteomics","volume":"22","author":"Zhao","year":"2023","journal-title":"J Proteome Res"},{"key":"2026092608242615300_ref2","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1093\/bib\/bbx141","article-title":"Proteomics and phosphoproteomics in precision medicine: applications and challenges","volume":"20","author":"Giudice","year":"2019","journal-title":"Brief Bioinform"},{"key":"2026092608242615300_ref3","doi-asserted-by":"publisher","first-page":"1603","DOI":"10.1038\/s41592-024-02343-1","article-title":"Quantms: a cloud-based pipeline for quantitative proteomics enables the reanalysis of public proteomics data","volume":"21","author":"Dai","year":"2024","journal-title":"Nat Methods"},{"key":"2026092608242615300_ref4","doi-asserted-by":"publisher","first-page":"1140","DOI":"10.1016\/j.cclet.2013.07.016","article-title":"A novel real-time cell electronic analysis technology for the authentication and quality control of natural medicines","volume":"24","author":"Yan","year":"2013","journal-title":"Chin Chem Lett"},{"key":"2026092608242615300_ref5","doi-asserted-by":"publisher","first-page":"4392","DOI":"10.1021\/acs.jproteome.4c00363","article-title":"A framework for quality control in quantitative proteomics","volume":"23","author":"Tsantilas","year":"2024","journal-title":"J Proteome Res"},{"key":"2026092608242615300_ref6","doi-asserted-by":"publisher","first-page":"4303","DOI":"10.1021\/acs.jproteome.4c00294","article-title":"Analysis and visualization of quantitative proteomics data using FragPipe-analyst","volume":"23","author":"Hsiao","year":"2024","journal-title":"J Proteome Res"},{"key":"2026092608242615300_ref7","doi-asserted-by":"publisher","first-page":"759","DOI":"10.1016\/j.cels.2021.06.006","article-title":"Artificial intelligence for proteomics and biomarker discovery","volume":"12","author":"Mann","year":"2021","journal-title":"Cell Syst"},{"key":"2026092608242615300_ref8","doi-asserted-by":"publisher","first-page":"1853","DOI":"10.1016\/j.cclet.2018.11.028","article-title":"A new quality control method for lateral flow assay","volume":"29","author":"Huang","year":"2018","journal-title":"Chin Chem Lett"},{"key":"2026092608242615300_ref9","doi-asserted-by":"publisher","first-page":"491","DOI":"10.3390\/biom13030491","article-title":"Quality control-a stepchild in quantitative proteomics: a case study for the human CSF proteome","volume":"13","author":"Rozanova","year":"2023","journal-title":"Biomolecules"},{"key":"2026092608242615300_ref10","doi-asserted-by":"publisher","first-page":"777","DOI":"10.1021\/acs.jproteome.5b00780","article-title":"Proteomics quality control: quality control software for MaxQuant results","volume":"15","author":"Bielow","year":"2016","journal-title":"J Proteome Res"},{"key":"2026092608242615300_ref11","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1080\/16843703.2024.2395745","article-title":"Comprehensive review of high-dimensional monitoring methods: trends, insights, and interconnections","volume":"22","author":"Ahmed","year":"2024","journal-title":"Qual Technol Quant Manag"},{"key":"2026092608242615300_ref12","doi-asserted-by":"publisher","first-page":"726","DOI":"10.1089\/cmb.2022.0243","article-title":"EnsMOD: a software program for omics sample outlier detection","volume":"30","author":"Manes","year":"2023","journal-title":"J Comput Biol"},{"key":"2026092608242615300_ref13","doi-asserted-by":"publisher","first-page":"btaf628","DOI":"10.1093\/bioinformatics\/btaf628","article-title":"PROTRIDER: protein abundance outlier detection from mass spectrometry-based proteomics data with a conditional autoencoder","volume":"41","author":"Klaproth-Andrade","year":"2025","journal-title":"Bioinformatics"},{"key":"2026092608242615300_ref14","doi-asserted-by":"publisher","first-page":"062","DOI":"10.21468\/SciPostPhys.16.3.062","article-title":"High-dimensional and permutation invariant anomaly detection","volume":"16","author":"Mikuni","year":"2024","journal-title":"SciPost Phys"},{"key":"2026092608242615300_ref15","volume-title":"Eighth IEEE International Conference on Data Mining","author":"Liu"},{"key":"2026092608242615300_ref16","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1145\/335191.335388","article-title":"LOF: identifying density-based local outliers","volume":"29","author":"Breunig","year":"2000","journal-title":"ACM SIGMOD Rec"},{"key":"2026092608242615300_ref17","volume-title":"Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data","author":"Ramaswamy"},{"key":"2026092608242615300_ref18","volume-title":"Advances in Knowledge Discovery and Data Mining","author":"Tang"},{"key":"2026092608242615300_ref19","volume-title":"Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","author":"Kriegel"},{"key":"2026092608242615300_ref20","doi-asserted-by":"publisher","first-page":"12181","DOI":"10.1109\/TKDE.2022.3159580","article-title":"ECOD: unsupervised outlier detection using empirical cumulative distribution functions","volume":"35","author":"Li","year":"2023","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2026092608242615300_ref21","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1007\/s10489-024-06092-8","article-title":"SDROF: outlier detection algorithm based on relative skewness density ratio outlier factor","volume":"55","author":"Zhang","year":"2024","journal-title":"Appl Intell"},{"key":"2026092608242615300_ref22","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1016\/j.jprot.2018.12.004","article-title":"Statistical analysis of proteomics data: a review on feature selection","volume":"198","author":"Lualdi","year":"2019","journal-title":"J Proteome"},{"key":"2026092608242615300_ref23","doi-asserted-by":"publisher","first-page":"btad021","DOI":"10.1093\/bioinformatics\/btad021","article-title":"Dealing with dimensionality: the application of machine learning to multi-omics data","volume":"39","author":"Feldner-Busztin","year":"2023","journal-title":"Bioinformatics"},{"key":"2026092608242615300_ref24","first-page":"4393","volume-title":"Proceedings of the 35th International Conference on Machine Learning","author":"Ruff","year":"2018"},{"key":"2026092608242615300_ref25","doi-asserted-by":"publisher","first-page":"118904","DOI":"10.1016\/j.eswa.2022.118904","article-title":"An efficient method for autoencoder based outlier detection","volume":"213","author":"Abhaya","year":"2023","journal-title":"Expert Syst Appl"},{"key":"2026092608242615300_ref26","author":"Kingma","journal-title":"2nd International Conference on Learning Representations"},{"key":"2026092608242615300_ref27","doi-asserted-by":"publisher","first-page":"12591","DOI":"10.1109\/TKDE.2023.3270293","article-title":"Deep isolation forest for anomaly detection","volume":"35","author":"Xu","year":"2023","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2026092608242615300_ref28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TKDE.2019.2905606","article-title":"Generative adversarial active learning for unsupervised outlier detection","volume":"32","author":"Liu","year":"2019","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2026092608242615300_ref29","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1038\/s41568-021-00408-3","article-title":"Harnessing multimodal data integration to advance precision oncology","volume":"22","author":"Boehm","year":"2022","journal-title":"Nat Rev Cancer"},{"key":"2026092608242615300_ref30","first-page":"1","volume-title":"An Introduction to Outlier Analysis. Outlier Analysis","author":"Aggarwal","year":"2017"},{"key":"2026092608242615300_ref31","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.1016\/S0167-8655(03)00003-5","article-title":"Discovering cluster-based local outliers","volume":"24","author":"He","year":"2003","journal-title":"Pattern Recognit Lett"},{"key":"2026092608242615300_ref32","volume-title":"Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining","author":"Lazarevic"},{"key":"2026092608242615300_ref33","volume-title":"Proceedings of the 2019 SIAM International Conference on Data Mining (SDM)","author":"Zhao"},{"key":"2026092608242615300_ref34","volume-title":"KI-2012: Poster and Demo Track","author":"Goldstein"},{"key":"2026092608242615300_ref35","doi-asserted-by":"publisher","first-page":"1443","DOI":"10.1162\/089976601750264965","article-title":"Estimating the support of a high-dimensional distribution","volume":"13","author":"Scholkopf","year":"2001","journal-title":"Neural Comput"},{"key":"2026092608242615300_ref36","first-page":"311","volume-title":"Foundations and Novel Approaches in Data Mining","author":"Shyu","year":"2006"},{"key":"2026092608242615300_ref37","first-page":"1","article-title":"PyoD: a python toolbox for scalable outlier detection","volume":"20","author":"Zhao","year":"2019","journal-title":"J Mach Learn Res"},{"key":"2026092608242615300_ref38","doi-asserted-by":"publisher","first-page":"e83","DOI":"10.1093\/nar\/gkaa498","article-title":"NAguideR: performing and prioritizing missing value imputations for consistent bottom-up proteomic analyses","volume":"48","author":"Wang","year":"2020","journal-title":"Nucleic Acids Res"},{"key":"2026092608242615300_ref39","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1038\/s41586-019-0987-8","article-title":"Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma","volume":"567","author":"Jiang","year":"2019","journal-title":"Nature"},{"key":"2026092608242615300_ref40","doi-asserted-by":"publisher","first-page":"bbae129","DOI":"10.1093\/bib\/bbae129","article-title":"SEAOP: a statistical ensemble approach for outlier detection in quantitative proteomics data","volume":"25","author":"Huang","year":"2024","journal-title":"Brief Bioinform"},{"key":"2026092608242615300_ref41","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1186\/s12859-025-06193-2","article-title":"Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-medoids clustering","volume":"26","author":"Wen","year":"2025","journal-title":"BMC Bioinformatics"},{"key":"2026092608242615300_ref42","doi-asserted-by":"publisher","first-page":"e217","DOI":"10.1016\/j.cell.2020.05.043","article-title":"Integrative proteomic characterization of human lung adenocarcinoma","volume":"182","author":"Xu","year":"2020","journal-title":"Cell"},{"key":"2026092608242615300_ref43","doi-asserted-by":"publisher","first-page":"1367","DOI":"10.1038\/nbt.1511","article-title":"MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification","volume":"26","author":"Cox","year":"2008","journal-title":"Nat Biotechnol"},{"key":"2026092608242615300_ref44","doi-asserted-by":"publisher","first-page":"D1522","DOI":"10.1093\/nar\/gkab1081","article-title":"iProX in 2021: connecting proteomics data sharing with big data","volume":"50","author":"Chen","year":"2022","journal-title":"Nucleic Acids Res"},{"key":"2026092608242615300_ref45","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1111\/2041-210X.13140","article-title":"The area under the precision-recall curve as a performance metric for rare binary events","volume":"10","author":"Sofaer","year":"2019","journal-title":"Methods Ecol Evol"},{"key":"2026092608242615300_ref46","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1007\/s00365-006-0663-2","article-title":"On early stopping in gradient descent learning","volume":"26","author":"Yao","year":"2007","journal-title":"Constr Approx"}],"container-title":["Briefings in Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/5\/bbag507\/71402447\/bbag507.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bib\/article-pdf\/27\/5\/bbag507\/71402447\/bbag507.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,26]],"date-time":"2026-09-26T12:31:41Z","timestamp":1790425901000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bib\/article\/doi\/10.1093\/bib\/bbag507\/8836554"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,1]]},"references-count":46,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,9,1]]}},"URL":"https:\/\/doi.org\/10.1093\/bib\/bbag507","relation":{},"ISSN":["1467-5463","1477-4054"],"issn-type":[{"value":"1467-5463","type":"print"},{"value":"1477-4054","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2026,9]]},"published":{"date-parts":[[2026,9,1]]},"article-number":"bbag507"}}