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MidClustpy: A Clustering Approach to Predict Coding Region in a Biological Sequence

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Soft Computing in Data Analytics

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 758))

Abstract

Data mining can act like a medium to discover new avenues in bioinformatics rather than just a pattern recognition in the biological sequences. It is useful in the sequence analysis, and clustering can be used to reduce the total number of operating sequences to perform this analysis. Expressed sequence tags (ESTs) are the complimentary DNA sequences, shorter in size and instrumental in locating coding region in genomic sequences. Clustering of these ESTs requires basic computer knowledge for sequence analysis and its relevance in the field of biology. MidClustpy is an algorithm specifically designed to cluster ESTs based on the most accurate part in the sequence. The similarity search for locating coding region in a query sequence can be assisted by MidClustpy algorithm. The research paper is, thus, focussed on the effective use of expressed sequence tags using MidClustpy for prediction of coding region.

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Author information

Authors and Affiliations

  1. G.H. Raisoni College of Engineering, Nagpur, 440016, India

    Neeta Maitre

  2. Rajiv Gandhi College of Engineering and Research, Nagpur, 441110, India

    Manali Kshirsagar

Authors
  1. Neeta Maitre
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  2. Manali Kshirsagar
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Corresponding author

Correspondence to Neeta Maitre .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India

    Janmenjoy Nayak

  2. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Washington, USA

    Ajith Abraham

  3. Department of Mechanical Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India

    B. Murali Krishna

  4. Department of Electrical and Electronics Engineering, Sri Sivani College of Engineering, Srikakulam, Andhra Pradesh, India

    G. T. Chandra Sekhar

  5. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology (IIEST), Shibpur, Howrah, West Bengal, India

    Asit Kumar Das

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© 2019 Springer Nature Singapore Pte Ltd.

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Maitre, N., Kshirsagar, M. (2019). MidClustpy: A Clustering Approach to Predict Coding Region in a Biological Sequence. In: Nayak, J., Abraham, A., Krishna, B., Chandra Sekhar, G., Das, A. (eds) Soft Computing in Data Analytics . Advances in Intelligent Systems and Computing, vol 758. Springer, Singapore. https://doi.org/10.1007/978-981-13-0514-6_35

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