In biology and medicine, various data, such as sequencing data, microarray, genotyping, 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 promises in business and computer sciences, but in biology and medical studies, such 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 of computer sciences and biology/medicine.

Potential topics include, but are not limited to:

• Predictive model of complex biological processes, such as alternative splicing and posttranslational 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
• Reviews of widely used machine learning and network methods for biologist