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Exploration of Deep Learning and Transfer Learning in Bioinformatics

Exploration of Deep Learning and Transfer Learning in Bioinformatics

Yash Mahajan, Muskan Sharma
Copyright: © 2024 |Pages: 21
ISBN13: 9798369318225|ISBN13 Softcover: 9798369345047|EISBN13: 9798369318232
DOI: 10.4018/979-8-3693-1822-5.ch012
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MLA

Mahajan, Yash, and Muskan Sharma. "Exploration of Deep Learning and Transfer Learning 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. 217-237. https://doi.org/10.4018/979-8-3693-1822-5.ch012

APA

Mahajan, Y. & Sharma, M. (2024). Exploration of Deep Learning and Transfer Learning 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. 217-237). IGI Global. https://doi.org/10.4018/979-8-3693-1822-5.ch012

Chicago

Mahajan, Yash, and Muskan Sharma. "Exploration of Deep Learning and Transfer Learning in Bioinformatics." In Applying Machine Learning Techniques to Bioinformatics: Few-Shot and Zero-Shot Methods, edited by Umesh Kumar Lilhore, et al., 217-237. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1822-5.ch012

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Abstract

In the dynamic field of bioinformatics, the fusion of deep learning and transfer learning techniques has ushered in a transformative era of discovery. Deep learning, powered by neural networks, has emerged as a formidable tool for deciphering intricate biological data, spanning genomics, proteomics, and metabolomics. Its capacity to automatically discern complex patterns and features within vast datasets has revolutionized our comprehension of biological processes. Transfer learning, a complementary approach, introduces a novel dimension to bioinformatics. By harnessing pre-trained models from domains like computer vision and natural language processing, researchers can expedite their analyses, reducing the demand for extensive labeled data. This cross-domain knowledge transfer expedites the progress of bioinformatics applications, from predicting protein structures to identifying disease biomarkers.

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