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Multiobjective Ranked Selection of Differentially Expressed Genes

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Proceedings of 2nd International Conference on Intelligent Computing and Applications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 467))

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Abstract

The regulatory action of a gene in a complex network is guided by the differential functionalities of the gene acting under varied conditions. Many methodologies have been proposed in recent years to unveil this regulation. However in this context the gene ranking obtained via separate methodologies according to their significance is quite dissimilar to one another making regulatory assessment of genes very difficult. In this paper, we have developed a novel procedure to compute significant genes using more than one ranking strategy. Accordingly, we have explored this idea applying the concept of non-dominated set of solutions residing in different Pareto optimal fronts. Our main objective is to find a set of non-dominated genes in the primary Pareto front each of which having an optimal combination of significant ranking across different ranking algorithms. In the results we have shown that most of the KEGG pathways formed from the set of DE genes contain at most two genes from the non-dominated set. This helps us to understand the independent regulatory function of a gene from the non-dominated set with the set of dominated genes. In other words, the existence of enriched control pathways with significant ranked genes non-dominant to one another is almost absent.

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Authors and Affiliations

  1. Department of ECE, D.I.A.T.M. Durgapur, Durgapur, India

    Mrityunjay Sarkar

  2. Department of ECE, N.I.T. Durgapur, Durgapur, India

    Aurpan Majumder

Authors
  1. Mrityunjay Sarkar
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  2. Aurpan Majumder
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Correspondence to Mrityunjay Sarkar .

Editor information

Editors and Affiliations

  1. KCG College of Technology, Dept. of Electrical and Electronics Engg KCG College of Technology, Karapakkam, Chennai, Tamil Nadu, India

    P. Deiva Sundari

  2. Dept Electrical & Electronics Engg, SRM University, Chennai, Tamil Nadu, India

    Subhransu Sekhar Dash

  3. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  4. Dept. of Electrical & Electronics Engg., Indian Institute of Technology Delhi, New Delhi, India

    Bijaya Ketan Panigrahi

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Sarkar, M., Majumder, A. (2017). Multiobjective Ranked Selection of Differentially Expressed Genes. In: Deiva Sundari, P., Dash, S., Das, S., Panigrahi, B. (eds) Proceedings of 2nd International Conference on Intelligent Computing and Applications. Advances in Intelligent Systems and Computing, vol 467. Springer, Singapore. https://doi.org/10.1007/978-981-10-1645-5_7

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  • DOI: https://doi.org/10.1007/978-981-10-1645-5_7

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-1644-8

  • Online ISBN: 978-981-10-1645-5

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