Skip To Main Content
Yeying Zhu on Muck Rack

Yeying Zhu

As seen in: Wiley Online Library, arXiv
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Yeying

Contact Yeying, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Computer Science > Computational Engineering, Finance, and Science

|
By Yeying Zhu, Peng Hu, Dafne Rozenberg
| arXiv
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Open in Who Shared View Duplicates (690) Issue with article?

The SlpX protein of Lactobacillus acidophilus CICC6074 has anti-inflammatory properties and protects Caco-2 intestinal epithelial cells from TNF-α-induced barrier damage

|
By Yeying Zhu, Engineering Ningbo Ningbo, YingYing Cao
| Wiley Online Library Verified
Zhu, Y., Cao, Y., Fan, X., Wu, Z., Shi, Z., Zhang, T., Cai, Z., Zeng, X. and Pan, D. (2026), The SlpX protein of Lactobacillus acidophilus CICC6074 has anti-inflammatory properties and protects Caco-2 intestinal epithelial cells from TNF-α-induced barrier damage. J Sci Food Agric.
Open in Who Shared Issue with article?

Noisy matrix completion for longitudinal data with subject‐ and time‐specific covariates

|
By Yeying Zhu, Joel Dubin, Zhaohan Sun
| Wiley Online Library Verified
1 Introduction Missing data problems are frequently encountered in epidemiology, environmental studies, and health research. According to Rubin (1976), there are three types of missingness: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Various methods have been proposed to handle different types of missingness, such as likelihood-based methods (Liu et al., 2022), imputation methods (Sportisse et al., 2020), and Bayesian approaches (Rubin, 1976).
Open in Who Shared Issue with article?
See all 4 articles