In biology and medicine, various data sets, such as those arising from sequencing data, microarray, genotype, and phenotype, are generated and released. But the straightforward traditional statistical analysis can only explore very limited perspectives of biological mechanisms. Advanced machine learning and network methods can be introduced to investigate more complex and hidden structures within the data and create big value out of the data. For example, deep learning has shown great promise in business and computer sciences, but in biology and medical studies, such a method has not been applied yet.

This Special Issue focuses on recent developments in machine learning and network methods and their applications in biology and medicine. We invite authors to contribute interdisciplinary papers in computer sciences and biology/medicine. Both original research and review articles are welcomed.

Potential topics include but are not limited to the following:

• Predictive models of complex biological processes, such as alternative splicing and post-translational modification
• Big data in biology and medicine
• Easy-to-use software for machine learning and network methods
• Reliable biomarker discovery
• Network based drug discovery
• Personalized medicine: choosing the right drug for the right patient
• Review of widely used machine learning and network methods for biologists