ArticleGenes2019
Identification of Essential Proteins Based on Improved HITS Algorithm.
Article in Genes, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Network Analysis of Biomarkers Associated with Occupational Exposure to Benzene and Malathion.International journal of molecular sciences · 2023Article
- Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs.Life (Basel, Switzerland) · 2022Article
- Article
- Revealing latent traits in the social behavior of distance learning students.Education and information technologies · 2022Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
- Network Analysis of Gene Transcriptions ofGenes · 2021Article
- Identifying essential proteins in dynamic protein networks based on an improved h-index algorithm.BMC medical informatics and decision making · 2020Article
- Predicting metabolite-disease associations based on KATZ model.BioData mining · 2019Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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
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