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International Conference on Swarm, Evolutionary, and Memetic Computing

SEMCCO 2015: Swarm, Evolutionary, and Memetic Computing pp 116–127Cite as

Analysis of Next-Generation Sequencing Data of miRNA for the Prediction of Breast Cancer

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9873)

Abstract

Recently, Next-Generation Sequencing (NGS) has emerged as revolutionary technique in the fields of ‘-omics’ research. The Cancer Research Atlas (TCGA) is a great example of it where massive amount of sequencing data is present for miRNA and mRNA. Analysing these data could bring out some potential biological insight. Moreover, developing a prognostic system based on this newly available sequencing data will give a greater help to cancer diagnosis. Hence, in this article, we have made an attempt to analyse such sequencing data of miRNA for accurate prediction of Breast Cancer. Generally miRNAs are small non-coding RNAs which are shown to participate in several carcinogenic processes either by tumor suppressors or oncogenes. This is the reason clinical treatment of the breast cancer patient has changed nowadays. Thus, it is interesting to understand the role of miRNAs for the prediction of breast cancer. In this regard, we have developed a technique using Gravitation Search Algorithm, which optimizes the underlying classification performance of Support Vector Machine. The proposed technique is able to select the potential features, in this case miRNAs, in order to achieve better prediction accuracy. In this study, we have achieved the classification accuracy upto 95.29 % by considering \({\simeq }\)1.5 % miRNAs of whole dataset automatically. Thereafter, a list of miRNAs is created after providing a rank. It is found from the list of top 15 miRNAs that 6 miRNAs are associated with the breast cancer while in others, 5 miRNAs are associated with different cancer types and 4 are unknown miRNAs. The performance of the proposed technique is compared with seven other state-of-the-art techniques. Finally, the results have been justified by the means of statistical test along with biological significance analysis of selected miRNAs.

Keywords

  • Breast cancer
  • Gravitation search algorithm
  • MicroRNA
  • Support vector machine
  • The Cancer Research Atlas

I. Saha and S.S. Bhowmick—Joint first authors and contributed equally.

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Notes

  1. 1.

    https://tcga-data.nci.nih.gov/tcga/.

  2. 2.

    http://mircancer.ecu.edu.

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Acknowledgment

This work was carried out during the tenure of an ERCIM ‘Alain Bensoussan’ Fellowship Programme as well as partially supported by the Polish National Science Centre (Grant number UMO-2013/09/B/NZ2/00121 and 2014/15/B/ST6/05082), COST BM1405 and BM1408 EU actions.

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Authors and Affiliations

  1. Institute of Informatics and Telematics, National Research Council, Pisa, Italy

    Indrajit Saha, Filippo Geraci & Marco Pellegrini

  2. National Institute of Technical Teachers’ Training and Research, Kolkata, India

    Indrajit Saha

  3. Centre of New Technologies, University of Warsaw, 02-097, Warsaw, Poland

    Indrajit Saha & Dariusz Plewczynski

  4. Department of Computer Science and Engineering, Jadavpur University, Kolkata, India

    Shib Sankar Bhowmick, Debotosh Bhattacharjee & Ujjwal Maulik

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  1. Indrajit Saha
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  2. Shib Sankar Bhowmick
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  3. Filippo Geraci
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  5. Debotosh Bhattacharjee
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  6. Ujjwal Maulik
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  7. Dariusz Plewczynski
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Correspondence to Indrajit Saha .

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Editors and Affiliations

  1. IIT, New Dehli, India

    Bijaya Ketan Panigrahi

  2. Nanyang Technological University, Singapore, Singapore

    Ponnuthurai Nagaratnam Suganthan

  3. Indian Statistical Institute, Kolkata, India

    Swagatam Das

  4. Department of Computer Science Engineering, Anil Neerukonda Institute of Technology and Sciences, Visakhapatnam, India

    Suresh Chandra Satapathy

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Saha, I. et al. (2016). Analysis of Next-Generation Sequencing Data of miRNA for the Prediction of Breast Cancer. In: Panigrahi, B., Suganthan, P., Das, S., Satapathy, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2015. Lecture Notes in Computer Science(), vol 9873. Springer, Cham. https://doi.org/10.1007/978-3-319-48959-9_11

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  • DOI: https://doi.org/10.1007/978-3-319-48959-9_11

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