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How can deep learning be used to improve bioinformatics? 


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Deep learning has revolutionized bioinformatics by enabling the extraction of useful information from complex and large datasets, such as genomics and bio-image informatics . In the field of protein-protein interactions (PPIs), deep learning models have been effective in predicting interaction sites, understanding disease mechanisms, and designing new drugs . Additionally, deep learning has been used to modernize classic bioinformatics tools by incorporating structural data and addressing data limitations through techniques like self-training . Overall, deep learning in bioinformatics has shown promise in improving pattern recognition, assisting in accurate diagnosis, and providing valuable insights into biological functions and disease processes.

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The paper discusses the development of vanGOGH, a geometric deep learning-based structural alignment approach, which uses deep learning to improve protein structural alignment in bioinformatics.
Deep learning can be used in bioinformatics to learn complicated nonlinear interactions between functionalities and extract information from large unlabeled data, improving protein-protein interaction analysis and prediction.
Deep learning models can be used in bioinformatics to learn complicated non-linear interactions between functionalities and extract information from large unlabeled data, improving protein-protein interaction analysis and prediction.
Book ChapterDOI
08 Feb 2023
Deep learning can be used in bioinformatics to find patterns, extract features, and provide solutions to complex problems in genomics and bio-image informatics.

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