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Incorporation of Kernel Support Vector Machine for Effective Prediction of Lysine Formylation from Class Imbalance Samples

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Proceedings of the International Conference on Big Data, IoT, and Machine Learning

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 95))

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Abstract

A post-translational modification (PTM) named lysine formylation discovered recently is a reversible and dynamic biological process primarily found on histone proteins of the organism that plays strong roles on modulation of chromatin conformations and the process of gene activation. A large number of traditional laboratory-based experimental methods and computational methods are currently available for identifying the formylated lysine residues. But the experimental methods are more costly and time consuming than computational methods. In order to predict formylated lysine sites, the existing computational methods are not satisfactory to select reliable non-formylated sites for balancing the training samples. A useful bioinformatics model named PLF_RNS is developed in this study by using various sequence-based features with support vector machine algorithm and F-score feature selection method. For this purpose, the verified formylated lysine samples are labeled as positive and reliable negative samples that are filtered from remaining samples using evolutionary information based on BLOSUM62 matrix are labeled as negative samples. The experimental result shows that PLF_RNS has acquired an average accuracy of 95.09% on tenfold cross validation, which is better compared to other currently available models. Therefore, it may be helpful for a better understanding of those types of molecular mechanisms and the development of drugs for related diseases.

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Authors and Affiliations

  1. Rajshahi University of Engineering and Technology, Rajshahi, Bangladesh

    Md. Sohrawordi & Md. Ali Hossain

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  1. Md. Sohrawordi
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  2. Md. Ali Hossain
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Editor information

Editors and Affiliations

  1. Chittagong University of Engineering and Technology (CUET), Chittagong, Bangladesh

    Mohammad Shamsul Arefin

  2. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  3. National Institute for Materials Science, Tsukuba, Japan

    Anirban Bandyopadhyay

  4. University of Dhaka, Dhaka, Bangladesh

    Md. Atiqur Rahman Ahad

  5. Amity University, Jaipur, India

    Kanad Ray

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Sohrawordi, M., Ali Hossain, M. (2022). Incorporation of Kernel Support Vector Machine for Effective Prediction of Lysine Formylation from Class Imbalance Samples. In: Arefin, M.S., Kaiser, M.S., Bandyopadhyay, A., Ahad, M.A.R., Ray, K. (eds) Proceedings of the International Conference on Big Data, IoT, and Machine Learning. Lecture Notes on Data Engineering and Communications Technologies, vol 95. Springer, Singapore. https://doi.org/10.1007/978-981-16-6636-0_15

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  • DOI: https://doi.org/10.1007/978-981-16-6636-0_15

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-6635-3

  • Online ISBN: 978-981-16-6636-0

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