Author

Andrew Koval

Benaroya Research Institute

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 2 Immunology· 1

Signature Assays, Organisms & Methods

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Organism:human· 2 Approach:statistical model· 2 Research Focus:clustering· 1 Research Focus:deconvolution· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Research Focus:gene regulatory network inference· 1 Organism:mouse· 1

Frequent Co-Authors

Ana M. AparicioiD· 1 shared Baohua ZhouiD· 1 shared Daniel CampbelliD· 1 shared Elya Ali ShamskhouiD· 1 shared Finnegan N Barry· 1 shared Gabriela Verdezoto· 1 shared Ivana K. KimiD· 1 shared John N. WeinsteiniD· 1 shared

Activity by Year

2023-2026 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 21 mutual co-author links

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1 shared paperBioinformatics

Ana M. AparicioORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

Dysfunctional effector programming in Foxp3-ΔE2 Tregs promotes enhanced CD8+ T cell responses

Kristin N. Weinstein, Zoe H. Bishop, Elya Ali Shamskhou, Finnegan N Barry, Gabriela Verdezoto, Marissa de Leon, Joseph R. Albe, Andrew Koval, Baohua Zhou, Phillip P. Domeier, Michael Y. Gerner, Daniel Campbell, Steven F. Ziegler

This study shows that Foxp3-{Delta}E2 Tregs have impaired suppressive function, leading to enhanced CD8+ T cell responses during inflammation.

bioRxiv · Oct 9, 2026 · PreprintImmunology

Accurate detection of tumor clonality and ongoing expansion mode from genomic data

Yanjie Chen, Roman Jaksik, Peter Terranova, Sara El Baghdadi, Andrew Koval, Monika Klara Kurpas, Simon Tavaré, Marek Kimmel, Khanh Ngoc Dinh

DECODE improves tumor clonality and expansion mode detection by accounting for sequencing biases and neutral tail in SFS, showing better performance on synthetic data and clinical relevance in glioma.

bioRxiv · Jun 19, 2026 · PreprintBioinformatics

DeMixSC: a deconvolution framework that uses single-cell sequencing plus a small benchmark dataset for improved analysis of cell-type ratios in complex tissue samples

Shuai Guo, Xiaoqian Liu, Xuesen Cheng, Yujie Jiang, Shuangxi Ji, Qingnan Liang, Andrew Koval, Yumei Li, Leah A. Owen, Ivana K. Kim, Ana M. Aparicio, John Paul Shen, Scott Kopetz, John N. Weinstein, Margaret M. DeAngelis, , Wenyi Wang

DeMixSC improves deconvolution accuracy by using single-cell RNA-seq and a benchmark dataset to account for technological discrepancies in complex tissue samples.

bioRxiv · Oct 10, 2023 · PreprintBioinformatics