Next Article in Journal
Genome-Wide Identification and Characterization of Olfactory Receptor Genes in Chinese Perch, Siniperca chuatsi
Previous Article in Journal
Role of β-Catenin Activation Levels and Fluctuations in Controlling Cell Fate
Previous Article in Special Issue
Reoptimized UNRES Potential for Protein Model Quality Assessment
Open AccessArticle

Identification of Essential Proteins Based on Improved HITS Algorithm

by Xiujuan Lei 1,*, Siguo Wang 1 and Fangxiang Wu 2,*
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
Department of Mechanical Engineering and Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N 5A9, Canada
Authors to whom correspondence should be addressed.
Genes 2019, 10(2), 177;
Received: 27 November 2018 / Revised: 9 February 2019 / Accepted: 19 February 2019 / Published: 25 February 2019
(This article belongs to the Special Issue Novel Approaches in Protein Structure Prediction)
Essential proteins are critical to the development and survival of cells. Identifying and analyzing essential proteins is vital to understand the molecular mechanisms of living cells and design new drugs. With the development of high-throughput technologies, many protein–protein interaction (PPI) data are available, which facilitates the studies of essential proteins at the network level. Up to now, although various computational methods have been proposed, the prediction precision still needs to be improved. In this paper, we propose a novel method by applying Hyperlink-Induced Topic Search (HITS) on weighted PPI networks to detect essential proteins, named HSEP. First, an original undirected PPI network is transformed into a bidirectional PPI network. Then, both biological information and network topological characteristics are taken into account to weighted PPI networks. Pieces of biological information include gene expression data, Gene Ontology (GO) annotation and subcellular localization. The edge clustering coefficient is represented as network topological characteristics to measure the closeness of two connected nodes. We conducted experiments on two species, namely Saccharomyces cerevisiae and Drosophila melanogaster, and the experimental results show that HSEP outperformed some state-of-the-art essential proteins detection techniques. View Full-Text
Keywords: essential proteins; HSEP; HITS algorithm; weighted PPI networks essential proteins; HSEP; HITS algorithm; weighted PPI networks
Show Figures

Figure 1

MDPI and ACS Style

Lei, X.; Wang, S.; Wu, F. Identification of Essential Proteins Based on Improved HITS Algorithm. Genes 2019, 10, 177.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop