ArticlePloS one2017
The integration of weighted gene association networks based on information entropy.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- CAISC: A software to integrate copy number variations and single nucleotide mutations for genetic heterogeneity profiling and subclone detection by single-cell RNA sequencing.BMC bioinformatics · 2022Article
- Identifying Cancer genes by combining two-rounds RWR based on multiple biological data.BMC bioinformatics · 2019Article
- scNPF: an integrative framework assisted by network propagation and network fusion for preprocessing of single-cell RNA-seq data.BMC genomics · 2019Article
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Authors and funding
6 authors.
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Abstract
Constructing genome scale weighted gene association networks (WGAN) from multiple data sources is one of research hot spots in systems biology. In this paper, we employ information entropy to describe the uncertain degree of gene-gene links and propose a strategy for data integration of weighted networks. We use this method to integrate four existing human weighted gene association networks and construct a much larger WGAN, which includes richer biology information while still keeps high functional relevance between linked gene pairs. The new WGAN shows satisfactory performance in disease gene prediction, which suggests the reliability of our integration strategy. Compared with existing integration methods, our method takes the advantage of the inherent characteristics of the component networks and pays less attention to the biology background of the data. It can make full use of existing biological networks with low computational effort.
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