Novel symmetry-based gene-gene dissimilarity measures utilizing Gene Ontology: Application in gene clustering.

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Novel symmetry-based gene-gene dissimilarity measures utilizing Gene Ontology: Application in gene clustering.

Gene. 2018 Dec 30;679:341-351

Authors: Acharya S, Saha S, Pradhan P

In recent years DNA microarray technology, leading to the generation of high-volume biological data, has gained significant attention. To analyze this high volume gene-expression data, one such powerful tool is Clustering. For any clustering algorithm, its efficiency majorly depends upon the underlying similarity/dissimilarity measure. During the analysis of such data often there is a need to further explore the similarity of genes not only with respect to their expression values but also with respect to their functional annotations, which can be obtained from Gene Ontology (GO) databases. In the existing literature, several novel clustering and bi-clustering approaches were proposed to identify co-regulated genes from gene-expression datasets. Identifying co-regulated genes from gene expression data misses some important biological information about functionalities of genes, which is necessary to identify semantically related genes. In this paper, we have proposed sixteen different semantic gene-gene dissimilarity measures utilizing biological information of genes retrieved from a global biological database namely Gene Ontology (GO). Four proximity measures, viz. Euclidean, Cosine, point symmetry and line symmetry are utilized along with four different representations of gene-GO-term annotation vectors to develop total sixteen gene-gene dissimilarity measures. In order to illustrate the profitability of developed dissimilarity measures, some multi-objective as well as single-objective clustering algorithms are applied utilizing proposed measures to identify functionally similar genes from Mouse genome and Yeast datasets. Furthermore, we have compared the performance of our proposed sixteen dissimilarity measures with three existing state-of-the-art semantic similarity and distance measures.

PMID: 30184472 [PubMed – indexed for MEDLINE]

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