Skip To Main Content
Faying Zhang on Muck Rack

Faying Zhang

As seen in: Wiley Online Library, ACS Publications, BioRxiv
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 Faying

Contact Faying, 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

Bright Single-Cell Fluorescent Reporter Enables Ultrasensitive Target Detection for Microbial Cell-Based Biosensors Click to copy article link Article link copied!

|
By Faying Zhang, Hui Zheng, Xuting Sun
| ACS Publications
Abstract Click to copy section linkSection link copied! Whole-cell biosensors (WCBs) capable of sensitively detecting trace amounts of analytes hold great potential for in situ detection of pollutants, toxins, or synthetic products.
Open in Who Shared Issue with article?

A smartphone-based genetically recombinant whole-cell biosensor for highly sensitive monitoring of polychlorinated biphenyls (PCBs)

|
By Qiangqiang Luo, Faying Zhang, Mengjie Zhang, Shantong Hu
| BioRxiv
Abstract Polychlorinated biphenyls (PCBs) are highly carcinogenic and persistent pollutants commonly found in ecosystems. Their complex congeners pose a huge challenge to instrumental analysis and ELISA methods, which prefer single and known targets. To overcome this limitation, here we developed an Escherichia coli whole-cell biosensor (WCB) for simultaneously detecting multiple PCB congeners.
Open in Who Shared Issue with article?

Adaptive kernel density estimation weighted twin support vector machine and its sample screening method

|
By Li Lv, Tanghuai Fan, Faying Zhang, Shenyu Qiu
| Wiley Online Library Verified
CONFLICT OF INTEREST STATEMENT The authors declare no conflicts of interest. REFERENCES 1. An overview of statistical learning theory. IEEE Trans Neural Netw. 1999; 10(5): 988-999. 2, . An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press; 2000. 3, . Twin support vector machines for pattern classification. IEEE Trans Pattern Analy Mach Intellig. 2007; 29(5): 905-910. 4, . Bi-density twin support vector machines for pattern recognition[J].
Open in Who Shared Issue with article?
See all 3 articles