Wenyu Zeng

she/her

PhD student in Biomedical & Health Informatics at the University of Washington. My research focuses on building scalable computational pipelines that integrate spatial transcriptomics and single-cell sequencing data to study glioblastoma heterogeneity. I’m passionate about combining biomedical data science and translational research to better understand tumor biology and identify therapeutic targets. Beyond research, I enjoy data visualization, teaching, and exploring new ways to make complex data accessible and meaningful.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Learning from Elders: Making an LLM-powered Chatbot for Retirement Communities more Accessible through User-centered Design

Published in arXiv preprint arXiv:2504.08985, 2025

Designing an accessible LLM-powered chatbot to support older adults in retirement communities using a human-centered approach.

Recommended citation: Li, L. X., Chung, R., Chen, F., Zeng, W., Jeon, Y., & Zaslavsky, O. (2025). "Learning from Elders: Making an LLM-powered Chatbot for Retirement Communities more Accessible through User-centered Design." arXiv preprint arXiv:2504.08985. https://doi.org/10.48550/arXiv.2504.08985
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Development and validation of PAMPA-BBB QSAR model to predict brain penetration potential of novel drug candidates

Published in Frontiers in Pharmacology 14:1291246, 2023

QSAR models trained on the largest PAMPA-BBB dataset to date to predict small-molecule brain penetration—an orthogonal complement to in vivo and Caco-2/MDCK assays.

Recommended citation: Kato, R., Zeng, W., Siramshetty, V. B., Williams, J., Kabir, M., Hagen, N., Padilha, E. C., Wang, A. Q., Mathé, E. A., Xu, X., & Shah, P. (2023). "Development and validation of PAMPA-BBB QSAR model to predict brain penetration potential of novel drug candidates." Frontiers in Pharmacology, 14:1291246. https://doi.org/10.3389/fphar.2023.1291246
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