Deep Learning in Bioinformatics

TL;DR

This paper reviews how deep learning techniques are transforming bioinformatics by analyzing various architectures and domains, highlighting current research, challenges, and future directions in the field.

Contribution

It provides a comprehensive categorization and analysis of deep learning applications across bioinformatics domains and architectures, offering insights for future research.

Findings

Deep learning achieves state-of-the-art results in bioinformatics tasks.

Categorization of research by domain and architecture enhances understanding.

Discussion of theoretical and practical issues guides future applications.

Abstract

In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e., omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e., deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies.