Author

Thao Nguyen-Tran

University of OttawaORCID 0000-0002-5507-618XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 3

Signature Assays, Organisms & Methods

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Approach:deep learning· 2 Assay:metabolomics· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Steffany A. L. BennettiD· 3 shared Theodore J. PerkinsiD· 3 shared Emily Hashimoto-RothiD· 2 shared Mathieu Lavallée‐AdamiD· 2 shared Michael G. OrganiD· 2 shared Angela M. CrawleyiD· 1 shared Anuradha SurendraiD· 1 shared Graeme P. Taylor· 1 shared

Activity by Year

2021-2026 · peak 2/yr

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3 shared papersBioinformatics

Steffany A. L. BennettORCID

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3 papers

Recent·Most cited

Desktop iDMS: A stand-alone application of intelligent differential mobility spectrometry (iDMS), a neural net-work that predicts optimal separation and compensa-tion voltages for field asymmetric differential ion mobil-ity spectrometry

Thao Nguyen-Tran, Emily Hashimoto-Roth, Xun-Xun Shi, Michael G. Organ, Theodore J. Perkins, Mathieu Lavallée‐Adam, Steffany A. L. Bennett

The paper introduces iDMS, a neural network that predicts optimal separation and compensation voltages for DMS, improving glycosphingolipid analysis.

bioRxiv · Oct 7, 2026 · PreprintBioinformatics

Intelligent differential ion mobility spectrometry (iDMS): A deep neural network that predicts optimal space-resolved ion mobility parameters for isomeric monoglycosphingolipids

Thao Nguyen-Tran, Xun Xun Shi, Emily Hashimoto-Roth, Michael G. Organ, Mathieu Lavallée‐Adam, Theodore J. Perkins, Steffany A. L. Bennett

The paper introduces iDMS, a deep neural network that predicts optimal ion mobility parameters for resolving monoglycosphingolipid stereoisomers, improving the efficiency of DMS-based assays.

bioRxiv · Sep 1, 2026 · PreprintBioinformatics

BATL: Bayesian annotations for targeted lipidomics

Justin G. Chitpin, Anuradha Surendra, Thao Nguyen-Tran, Graeme P. Taylor, Hongbin Xu, Irina Alecu, Roberto Ortega, Julianna J. Tomlinson, Angela M. Crawley, Michaeline McGuinty, Michael G. Schlossmacher, Rachel J. Saunders-Pullman, Miroslava Čuperlović‐Culf, Steffany A. L. Bennett, Theodore J. Perkins

BATL is a Bayesian classifier for targeted lipidomics that annotates LC-ESI-MS/MS peaks with high accuracy using eight features, achieving over 95% correct identification in SRM datasets.

Bioinformatics · Dec 21, 2021Peer-reviewedBioinformatics