Deep Learning in Bioinformatics: Techniques and Applications in Practice

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ASIN ‏ : ‎ B09Q33LHZ9
Publisher ‏ : ‎ Academic Press (January 8, 2022)
Publication date ‏ : ‎ January 8, 2022
Language ‏ : ‎ English
File size ‏ : ‎ 134522 KB
Text-to-Speech ‏ : ‎ Enabled
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Enabled
Print length ‏ : ‎ 362 pages

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Deep Learning in Bioinformatics: Techniques and Applications in Practice

Bioinformatics is a rapidly evolving field that combines biology, computer science, and statistics to analyze and interpret biological data. Deep learning, a subset of machine learning, has gained popularity in recent years due to its ability to extract complex patterns and features from large datasets. In bioinformatics, deep learning techniques have been applied to a wide range of problems, including genomics, proteomics, and drug discovery.

One of the key advantages of deep learning in bioinformatics is its ability to handle high-dimensional data, such as DNA sequences and gene expression profiles. Traditional machine learning algorithms often struggle with these types of data, but deep learning models, such as convolutional neural networks and recurrent neural networks, can effectively capture the underlying patterns in biological data.

In practice, deep learning has been used in bioinformatics for a variety of applications. For example, deep learning models have been developed to predict protein structure and function, identify disease-associated genetic mutations, and classify different types of cancer based on gene expression data. These applications have the potential to revolutionize the field of bioinformatics by providing more accurate and efficient tools for analyzing biological data.

Overall, deep learning in bioinformatics holds great promise for advancing our understanding of complex biological systems and developing new treatments for diseases. By leveraging the power of deep learning techniques, researchers can uncover hidden patterns in biological data that were previously inaccessible, leading to new insights and discoveries in the field of bioinformatics.
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