Author

Lindsay Grey Cowell

The University of Texas Southwestern Medical CenterORCID 0000-0003-1617-8244Unclaimed 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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Research Focus:database / resource· 2 Research Focus:benchmarking· 1 Organism:human· 1 Approach:machine learning (classical)· 1 Organism:non-human primate· 1 Assay:proteomics· 1 Approach:statistical model· 1

Frequent Co-Authors

Bjoern PetersiD· 2 shared Brian CorrieiD· 2 shared Lonneke SchefferiD· 2 shared Randi VitaiD· 2 shared Scott ChristleyiD· 2 shared Alan RuttenbergiD· 1 shared Alessandro SetteiD· 1 shared Alexander D. DiehliD· 1 shared

Activity by Year

2021-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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2 shared papersBioinformatics

Bjoern PetersORCID

Mutual co-authors in cluster

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3 papers

Recent·Most cited

BioinformaticsbioRxivPreprintJun 6, 2026

Revised Adaptive Immune Receptor Data in the Immune Epitope Database

Lonneke Scheffer, Eve Richardson, Randi Vita…·Bjoern Peters

The paper revises the standardization and validation pipeline for immune receptor data in the IEDB to improve data consistency and usability.

1 citation

proteomicsstatistical model

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BioinformaticsScientific DataPeer-reviewedApr 30, 2026

VO: The Vaccine Ontology

Jie Zheng, Yu Lin, Anthony Huffman…·Yongqun Oliver He

The paper introduces the Vaccine Ontology (VO), a standardized framework for representing vaccines and related data, enhancing data integration and AI research.

3 citations

humannon-human primate

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BioinformaticsNature Machine IntelligencePeer-reviewedNov 16, 2021

immuneML: an ecosystem for machine learning analysis of adaptive immune receptor repertoires

Milena Pavlović, Lonneke Scheffer, Keshav Motwani…·Geir Kjetil Sandve

immuneML provides an open-source ecosystem for reproducible and interoperable machine learning analysis of adaptive immune receptor repertoires, demonstrating its utility through benchmarking and novel methods.

87 citationstop 3%

machine learning (classical)benchmarking

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