| Quantitative clinical glycomics strategies: a guide for selecting the best analysis approach MW Patabandige, LD Pfeifer, HT Nguyen, H Desaire Mass spectrometry reviews 41 (6), 901-921, 2022 | 17 | 2022 |
| The Aristotle Classifier: Using the whole glycomic profile to indicate a disease state D Hua, MW Patabandige, EP Go, H Desaire Analytical chemistry 91 (17), 11070-11077, 2019 | 16 | 2019 |
| Two new tools for glycopeptide analysis researchers: a glycopeptide decoy generator and a large data set of assigned CID spectra of glycopeptides JC Lakbub, X Su, Z Zhu, MW Patabandige, D Hua, EP Go, H Desaire Journal of proteome research 16 (8), 3002-3008, 2017 | 13 | 2017 |
| Clinically viable assay for monitoring uromodulin glycosylation MW Patabandige, EP Go, H Desaire Journal of the American Society for Mass Spectrometry 32 (2), 436-443, 2020 | 9 | 2020 |
| The local-balanced model for improved machine learning outcomes on mass spectrometry data sets and other instrumental data H Desaire, MW Patabandige, D Hua Analytical and bioanalytical chemistry 413, 1583-1593, 2021 | 4 | 2021 |
| Leveraging R (LevR) for fast processing of mass spectrometry data and machine learning: Applications analyzing fingerprints and glycopeptides LD Pfeifer, MW Patabandige, H Desaire Frontiers in Analytical Science 2, 961592, 2022 | 3 | 2022 |
| Assessment of monoclonal antibody glycosylation: a comparative study using HRMS, NMR, and HILIC-FLD J Shipman, M Karfunkle, H Zhu, Y Zhuo, K Chen, M Patabandige, D Wu, ... Analytical and Bioanalytical Chemistry 416 (13), 3127-3137, 2024 | | 2024 |
