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International Joint Conference on Rough Sets

IJCRS 2017: Rough Sets pp 587–598Cite as

Integration of Gene Expression and Ontology for Clustering Functionally Similar Genes

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10313))

Abstract

Clustering functionally similar genes helps in understanding the mechanism of a biological pathway. It also provides information of those genes whose biological importance is previously not known. Clustering of genes is highly dependent on the similarity or dissimilarity criterion. Usually, microarray gene expression data is used to cluster genes. However, a microarray data may contain noise that may lead to undesired results. Therefore, incorporating gene ontology information may improve the clustering solutions. In this regard, an integrated dissimilarity measure is introduced for grouping functionally similar genes. It is comprised of city block distance and gene ontology based semantic dissimilarity. While, the city block distance is used to compute distance between two gene expression vectors, gene ontology based semantic dissimilarity measure is used for incorporating biological knowledge. The importance of the integrated dissimilarity measure is shown by incorporating it in different c-means clustering algorithms including rough-fuzzy clustering algorithms. In this work it has been shown that incorporation of integrated dissimilarity measure increases the functional similarity of cluster of genes as compared to the methods that are based on either type of dissimilarity measure. It is also observed that the rough-fuzzy clustering algorithm performs better with the new dissimilarity measure compared to different c-means clustering algorithms.

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Acknowledgements

The author wants to acknowledge Dr. Pradipta Maji of Indian Statistical Institute, Kolkata, India for his valuable suggestions.

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Authors and Affiliations

  1. Department of Bioscience and Bioengineering, Indian Institute of Technology Jodhpur, Jodhpur, India

    Sushmita Paul

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  1. Sushmita Paul
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Correspondence to Sushmita Paul .

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Editors and Affiliations

  1. Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Lech Polkowski

  2. University of Regina, Regina, SK, Canada

    Yiyu Yao

  3. University of Warmia and Mazury, Olsztyn, Poland

    Piotr Artiemjew

  4. University of Milano-Bicocca, Milano, Italy

    Davide Ciucci

  5. Southwest Jiaotong University, Chengdu, China

    Dun Liu

  6. Warsaw University, Warszawa, Poland

    Dominik Ślęzak

  7. Silesian University, Sosnowiec, Poland

    Beata Zielosko

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Paul, S. (2017). Integration of Gene Expression and Ontology for Clustering Functionally Similar Genes. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_47

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  • DOI: https://doi.org/10.1007/978-3-319-60837-2_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60836-5

  • Online ISBN: 978-3-319-60837-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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