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Here, we devised a three\u2010step approach that prioritized retaining biologically\u2010relevant information in EV samples using a dataset containing two populations of EVs analysed by liquid chromatography electrospray ionization tandem mass spectrometry (LC\u2010ESI\u2010MS\/MS). Firstly, to avoid overfitting, we excluded proteins in which more than half of the values were missing. Next, we statistically tested for low protein abundance in a single population: when the number of potential \u201cmissing not at random\u201d (MNAR) values was significantly enriched, these values were replaced with the lowest possible value of \u201c1\u201d. Finally, all remaining missing values were then imputed using the Random Forest machine learning algorithm. Our final dataset included 49.9 % of proteins that originally contained at least one missing value, from which 84.7 % were listed in the ExoCarta database, significantly more than would be expected by chance, strongly indicating biologically relevant imputation. To enable other EV researchers to analyse proteomics data in a robust, easy\u2010to\u2010use and peer\u2010reviewed manner, we provide BioProEV, a bioinformatics pipeline to impute missing values with biological relevance in EV datasets.<\/jats:p>","DOI":"10.1002\/jex2.70150","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T17:43:16Z","timestamp":1778866996000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["BioProEV: A Bioinformatics Pipeline for Biologically\u2010Relevant Handling of Missing Values in the Analysis of Extracellular Vesicles by Mass Spectrometry"],"prefix":"10.1002","volume":"5","author":[{"given":"P\u00e2mella","family":"Miranda","sequence":"first","affiliation":[{"name":"Department of Women's and Children's Health Karolinska Institutet  Stockholm Sweden"},{"name":"Astrid Lindgren Children's Hospital  Stockholm 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