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Adaptive Machine Learning Algorithm and Analytics of Big Genomic Data for Gene Prediction

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Tracking and Preventing Diseases with Artificial Intelligence

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 206))

Abstract

Artificial intelligence helps in tracking and preventing diseases. For instance, machine learning algorithms can analyze big genomic data and predict genes, which helps researchers and scientists to gain deep insights about protein-coding genes in viruses that cause certain diseases. To elaborate, prediction of protein-coding genes from the genome of organisms is important to the synthesis of protein and the understating of the regulatory function of the non-coding region. Over the past few years, researchers have developed methods for finding protein-coding genes. Notwithstanding, the recent data explosion in genomics accentuates the need for efficient gene prediction algorithms. This book chapter presents an adaptive naive Bayes-based machine learning (NBML) algorithm to deploy over a cluster of the Apache Spark framework for efficient prediction of genes in the genome of eukaryotic organisms. To evaluate the NBML algorithm on its discovery of the protein-coding genes from the human genome chromosome GRCh37, a confusion matrix was constructed and its results show that NBML led to high specificity, precision and accuracy of 94.01%, 95.04% and 96.02%, respectively. Moreover, the algorithm can be effective for transfer knowledge in new genomic datasets.

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Notes

  1. 1.

    https://www.ddbj.nig.ac.jp/index-e.html.

  2. 2.

    https://www.ebi.ac.uk/.

  3. 3.

    https://flybase.org/.

  4. 4.

    http://mirbase.org/.

  5. 5.

    https://www.ncbi.nlm.nih.gov/.

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Acknowledgements

This project is partially supported by (a) Association of Commonwealth Universities (ACU), (b) Natural Sciences and Engineering Research Council of Canada (NSERC), and (c) University of Manitoba.

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Authors and Affiliations

  1. University of Manitoba, Winnipeg, MB, Canada

    Oluwafemi A. Sarumi & Carson K. Leung

  2. The Federal University of Technology—Akure (FUTA), Akure, Nigeria

    Oluwafemi A. Sarumi

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  1. Oluwafemi A. Sarumi
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  2. Carson K. Leung
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Correspondence to Carson K. Leung .

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Editors and Affiliations

  1. Department of Computer Engineering, Sarvajanik College of Engineering and Technology, Surat, Gujarat, India

    Mayuri Mehta

  2. School of Humanities and Social Sciences, Harbin Institute of Technology (Shenzhen), Shenzhen, Guangdong, China

    Philippe Fournier-Viger

  3. Department of Computer Engineering, G. H. Patel College of Engineering and Technology, Charutar Vidya Mandal University, Vallabh Vidyanagar, Gujarat, India

    Maulika Patel

  4. Department of Computer Science, Electrical Engineering and Mathematical Sciences, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

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Sarumi, O.A., Leung, C.K. (2022). Adaptive Machine Learning Algorithm and Analytics of Big Genomic Data for Gene Prediction. In: Mehta, M., Fournier-Viger, P., Patel, M., Lin, J.CW. (eds) Tracking and Preventing Diseases with Artificial Intelligence. Intelligent Systems Reference Library, vol 206. Springer, Cham. https://doi.org/10.1007/978-3-030-76732-7_5

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