Machine Learning in Bioinformatics: Current Research and Development

Dear Colleagues,

Due to the revolution of high-throughput experimental techniques, the amount of biological data have been exponentially growing in recent years. This raises the urgent need for developing computational methods and software tools to understand these data and further extract useful information from them. Bioinformatics is an interdisciplinary field that is advancing in order to reach these goals. Machine learning is one of the most popular methodologies in bioinformatics. It consists of a large variety of algorithms to discover underlying patterns in biological systems by learning from existing data. These algorithms include, but are not limited to, supervised learning methods, such as back-propagation neural networks and support vector machines, for the classification of genomic and proteomic data; unsupervised learning methods, such as self-organizing maps, to cluster, visualize, and reduce the complexity of high-dimensional biological or clinical datasets; and reinforcement learning to optimize the outcome of various biomedical problems, such as drug design and health management. Moreover, the field of bioinformatics has greatly benefited from the recent advances in deep learning, such as large language models (LLM). The versatile applications of these models in analyzing different biological data have drawn enormous attention and gained huge successes in fields including protein structure prediction. For instance, transformer models based on the attention mechanism have already been used in fields such as multi-omics and spatial transcriptomics.

This Special Issue on “Machine Learning in Bioinformatics: Current Research and Development” focuses on the fundamental method development of machine learning methods and their practical applications in all fields of bioinformatics, from genome annotation, function prediction, imaging analysis, structural bioinformatics, systems biology, single cell data processing, and database integration to computational evolutionary biology. Potential topics of interest include but are not limited to the following:

• Machine learning applications in single-cell genomics and transcriptomics data analysis;
• Machine learning-assisted drug discovery;
• Analysis of biological network (including protein–protein interaction networks, gene regulatory networks, cell signaling networks and metabolic networks) with deep learning;
• Development of reinforcement learning strategies for understanding biological systems;
• Deep generative models for protein and peptide design;
• Machine learning-based discovery of disease-related biomarkers;
• Applications of machine learning in translational medicine and health care;
• Machine learning-aided analysis of super-resolution microscopic data;
• Applications of large language models in bioinformatics;
• Transformer-based deep learning for the predicting of biomolecular properties;
• Integration of machine learning to numerical simulations of biological systems.

Dr. Yinghao Wu
Dr. Kalyani Dhusia
Dr. Zhaoqian Su
Guest Editors

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• high-throughput experimental techniques
• bioinformatics
• machine learning
• supervised learning
• back-propagation neural networks
• support vector machines
• unsupervised learning
• self-organizing maps
• reinforcement learning
• deep learning
• large language models (LLM)