Deep learning in bioinformatics

Abstract

Deep learning is a powerful machine learning technique that can learn from large amounts of data using multiple layers of artificial neural networks. This paper reviews some applications of deep learning in bioinformatics, a field that deals with analyzing and interpreting biological data. We first introduce the basic concepts of deep learning and then survey the recent advances and challenges of applying deep learning to various bioinformatics problems, such as genome sequencing, gene expression analysis, protein structure prediction, drug discovery, and disease diagnosis. We also discuss future directions and opportunities for deep learning in bioinformatics. We aim to provide an overview of deep learning so that bioinformaticians applying deep learning models can consider all critical technical and ethical aspects. Thus, our target audience is biomedical informatics researchers who use deep learning models for inference. This review will inspire more bioinformatics researchers to adopt deep-learning methods for their research questions while considering fairness, potential biases, explainability, and accountability.

Keywords: Deep learning; bioinformatics; biological data analysis; neural networks.

Figure 1

Relationships among bioinformatics, information science, artificial intelligence, machine learning, and deep learning. At the intersection of all circles (orange) is the application of AI, ML, and DL in other areas, such as bioinformatics.

Figure 2

Fully connected deep learning network. The neurons of the input layer are in orange, those of the hidden layers are in blue, and the neurons of the output layer are in green. The connections in this example are directed from input to output and are indicated by arrows. Each arrow represents a trainable weight.