Skip to main content
Zilan Ning on Muck Rack

Zilan Ning

As seen in: MDPI, PeerJ
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 Zilan

Contact Zilan, 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

Applied Sciences

|
By Dan Cao, Zilan Ning, Xueyan Liu, Zongtao Wang
| MDPI
Special Issue Editors Dr. Tao Wang E-Mail Website Guest Editor School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China Interests: bioinformatics; machine learning; network modeling; deep learning; big data mining Dr. Jiajie Peng E-Mail Website Guest Editor School of Computer Science, Northwestern Polytechnical University, Xi’an 710072, China Interests: machine learning; network modeling; deep learning Dr. Yongtian Wang E-Mail Website Guest Editor School of...
Open in Who Shared Issue with article?

A Novel Prognostic Model for Gastric Cancer with EP_Dis-Based Co-Expression Network Analysis

|
By Yalan Xu, Hongyan Zhang, Dan Cao, Zilan Ning
| MDPI
Abstract : Ferroptosis is a regulated form of cell death that involves iron-dependent lipid peroxidation. Ferroptosis-related genes (FRGs) play an essential role in the tumorigenesis of gastric cancer (GC), which is one of the most common and lethal cancers worldwide. Understanding the prognostic significance of FRGs in GC can shed light on GC treatment and diagnosis. In this study, we proposed a new gene co-expression network analysis method, namely EP-WGCNA.
Open in Who Shared Issue with article?

A clustering method for small scRNA-seq data based on subspace and weighted distance

|
By Hunan Agricultural, Andrew TS, Zilan Ning, Hunan Engineering
| PeerJ
Discussion of prevailing methods We also provided further discussion in Tables 4–5. The average performance of SC3 was only lower than SSWD, and its results were not significantly different from SSWD in the one-sided Wilcoxon signed-rank test. The SC3 combined multiple similarity measures (Euclidean, Pearson, Spearman) in clustering. It used the consistency matrix to integrate the multiple clustering results, and the consistency matrix strengthened the consensus signal between cells.
Open in Who Shared Issue with article?
See all 3 articles