Ranking differential hubs in gene co-expression networks

Journal of Bioinformatics and Computational Biology
Omar Odibat, Chandan K Reddy

Abstract

Identifying the genes that change their expressions between two conditions (such as normal versus cancer) is a crucial task that can help in understanding the causes of diseases. Differential networking has emerged as a powerful approach to detect the changes in network structures and to identify the differentially connected genes among two networks. However, existing differential network-based methods primarily depend on pairwise comparisons of the genes based on their connectivity. Therefore, these methods cannot capture the essential topological changes in the network structures. In this paper, we propose a novel algorithm, DiffRank, which ranks the genes based on their contribution to the differences between the two networks. To achieve this goal, we define two novel structural scoring measures: a local structure measure (differential connectivity) and a global structure measure (differential betweenness centrality). These measures are optimized by propagating the scores through the network structure and then ranking the genes based on these propagated scores. We demonstrate the effectiveness of DiffRank on synthetic and real datasets. For the synthetic datasets, we developed a simulator for generating synthetic differentia...Continue Reading

References

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Citations

Aug 29, 2013·Evidence-based Complementary and Alternative Medicine : ECAM·Ming YangGuang Ji
Feb 3, 2015·Knowledge and Information Systems·Omar Odibat, Chandan K Reddy
Sep 7, 2016·BioMed Research International·Junyi LiYuan-Yuan Li
Oct 27, 2017·Bioinformatics·Karan UppalJonathan Wren
Jan 14, 2021·Interdisciplinary Sciences, Computational Life Sciences·Jiang XieJiao Wang

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