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Deep Learning in Bioinformatics: Principles and Applications
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Author(s): Alessandra G. Cioletti (Federal University of Minas Gerais, Brazil), Frederico C. Carvalho (Federal University of Minas Gerais, Brazil), Lucas M. Dos Santos (Federal University of Minas Gerais, Brazil)and Raquel C. M. Minardi (Federal University of Minas Gerais, Brazil)
Copyright: 2025
Pages: 32
Source title:
Effective Techniques for Bioinformatic Exploration
Source Author(s)/Editor(s): Paulo Fazendeiro (Instituto de Telecomunicações, Portugal & University of Beira Interior, Portugal)and Carmelina Leite (CEO of NEW APPROACH — Machine Learning for Pharma and Nutraceuticals)
DOI: 10.4018/979-8-3693-3192-7.ch005
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Abstract
The power of deep learning-based models to process and interpret complex biological data has opened new avenues for research and discovery, offering innovative solutions to longstanding and emerging challenges in bioinformatics. This chapter explores how deep learning is researchers solve some of the most pressing challenges in bioinformatics today. From predicting protein structures to speeding the development of new drugs, these models are at the forefront of bioinformatic research, redefining the boundaries of the. The authors explore applications where deep learning has made significant contributions and the synergy between deep learning techniques and bioinformatics, illustrating how this powerful collaboration is helping to advance medical research, drug discovery, and beyond. The current challenges and limitations of such methods are also covered in this chapter. The goal is to provide insights into the transformative impact of deep learning on bioinformatics, underscoring its potential to revolutionize our understanding of life.
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