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Machine Learning's Potential in Shaping the Future of Bioinformatics Research

Machine Learning's Potential in Shaping the Future of Bioinformatics Research

V. Dankan Gowda, Saptarshi Mukherjee, Sajja Suneel, Dinesh Arora, Ujjwal Kumar Kamila
Copyright: © 2024 |Pages: 22
ISBN13: 9798369318225|ISBN13 Softcover: 9798369345047|EISBN13: 9798369318232
DOI: 10.4018/979-8-3693-1822-5.ch015
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MLA

Gowda, V. Dankan, et al. "Machine Learning's Potential in Shaping the Future of Bioinformatics Research." Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods, edited by Umesh Kumar Lilhore, et al., IGI Global, 2024, pp. 281-302. https://doi.org/10.4018/979-8-3693-1822-5.ch015

APA

Gowda, V. D., Mukherjee, S., Suneel, S., Arora, D., & Kamila, U. K. (2024). Machine Learning's Potential in Shaping the Future of Bioinformatics Research. In U. Lilhore, A. Kumar, S. Simaiya, N. Vyas, & V. Dutt (Eds.), Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods (pp. 281-302). IGI Global. https://doi.org/10.4018/979-8-3693-1822-5.ch015

Chicago

Gowda, V. Dankan, et al. "Machine Learning's Potential in Shaping the Future of Bioinformatics Research." In Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods, edited by Umesh Kumar Lilhore, et al., 281-302. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1822-5.ch015

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Abstract

In recent years, the application of machine learning techniques has brought about a profound transformation in the field of bioinformatics. This chapter is dedicated to examining the latest developments in both bioinformatics and machine learning while also exploring potential future directions. Within these pages, the authors delve into the potential advantages of employing machine learning to enhance critical bioinformatics tasks, such as the analysis of genomic sequences and the prediction of protein structures through modeling. The chapter also addresses the challenges faced by researchers when integrating these diverse fields. Nevertheless, optimism prevails in the realm of bioinformatics research, driven by the ever-expanding wealth of biological data and the potential for the development of more sophisticated machine learning models.

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