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Exploration of Deep Learning and Transfer Learning Techniques in Bioinformatics

Exploration of Deep Learning and Transfer Learning Techniques in Bioinformatics

Sumit Bansal, Vandana Sindhi, Bhim Sain Singla
Copyright: © 2024 |Pages: 20
ISBN13: 9798369318225|ISBN13 Softcover: 9798369345047|EISBN13: 9798369318232
DOI: 10.4018/979-8-3693-1822-5.ch013
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MLA

Bansal, Sumit, et al. "Exploration of Deep Learning and Transfer Learning Techniques in Bioinformatics." Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods, edited by Umesh Kumar Lilhore, et al., IGI Global, 2024, pp. 238-257. https://doi.org/10.4018/979-8-3693-1822-5.ch013

APA

Bansal, S., Sindhi, V., & Singla, B. S. (2024). Exploration of Deep Learning and Transfer Learning Techniques in Bioinformatics. 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. 238-257). IGI Global. https://doi.org/10.4018/979-8-3693-1822-5.ch013

Chicago

Bansal, Sumit, Vandana Sindhi, and Bhim Sain Singla. "Exploration of Deep Learning and Transfer Learning Techniques in Bioinformatics." In Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods, edited by Umesh Kumar Lilhore, et al., 238-257. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1822-5.ch013

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Abstract

The blend of profound learning and more learning techniques has brought about a worldview change in the creating subject of bioinformatics. The revolutionary environment that emerges when the complexity of biological data and artificial intelligence meet is the subject of this book chapter. The section explores profound learning models and moves learning closer, as well as the imaginative applications, hardships, and achievements that have developed at the crossing point of these two powerful fields. The section opens with a point-by-point assessment of profound learning standards and how they are applied to the unique difficulties of bioinformatics datasets. The section likewise digs into the idea of move learning—a strong worldview that utilizes information learned in one space to further develop execution in another. It has been shown that movement learning is powerful. The section likewise dives into the idea of move learning—a strong worldview that involves mastery in one region to further develop execution in another.

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