Molecular biology disciplines have witnessed an explosive growth of biological data in the past few decades. Biophysical and biochemical techniques help to precisely determine the biological data. Unfortunately, these techniques are not appropriate for processing data on a large scale, for example, on omics data. This comes with the need to implement novel approaches to address data generation, cleaning, analysis, and sharing. The substantial and growing gap between the number of known and unknown biological data motivates the need for high-throughput approaches.

The rapid development of bioinformatics technologies makes biochemical and biosystems engineering more efficient than ever before. These technologies use in-silico methods such as reconstruction, molecular modeling, pattern recognition, machine learning, and deep learning to address multitudes of challenges related to dimensionality curse, data noises, data scalability, and data processing in biology and medicinal drug design.

This Special Issue aims to provide an insight into the latest novel high-throughput approaches that focus on the challenges of big data in bioinformatics. It will collect mathematical, statistical, and intelligent techniques to tackle problems in systems biology, comparative proteomics, structural genomics, biomedical engineering, and biosystems engineering. We welcome both original research and review articles that cover state-of-the-art advances in this rapidly developing area.

Potential topics include but are not limited to the following:

• High-throughput protein structure and function prediction
• Analysis and prediction of intrinsic disorder
• Target selection methods for structural genomics
• Biomedical big data analytics
• In silico prediction and mapping of epitope
• Medical imaging and hybrid imaging
• Algorithms optimization
• Machine learning and deep learning applications
• Patient privacy protection and data sharing
• Biomedical and biosystems engineering