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RelSim: An integrated method to identify disease genes using gene expression profiles and PPIN based similarity measure
P. Maji, E. Shah,
Published in Elsevier Inc.
Volume: 384
Pages: 110 - 125
One of the important problems in functional genomics is how to select the disease genes. In this regard, the paper presents a new gene selection algorithm, termed as RelSim, to identify disease genes. It integrates judiciously the information of gene expression profiles and protein-protein interaction networks. A new similarity measure is introduced to compute the functional similarity between two genes. It is based on the information of protein-protein interaction networks. The new similarity measure offers an efficient way to calculate the functional similarity between two genes. The proposed algorithm selects a set of genes as disease genes, considering both microarray and protein-protein interaction data, by maximizing the relevance and functional similarity of the selected genes. While gene expression profiles are used to identify differentially expressed genes, the protein-protein interaction networks help to compute the functional similarity among genes. The performance of the proposed algorithm, along with a comparison with other related methods, is demonstrated on several colon cancer data sets. © 2016 Elsevier Inc.
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JournalData powered by TypesetInformation Sciences
PublisherData powered by TypesetElsevier Inc.
Open AccessNo