Nature-inspired computing refers to computational methods that are mainly dedicated to the development of new models for studying how naturally occurring phenomena behave to address complex problems. This innovative research area encompasses several research branches, such as evolutionary computation, neural networks, artificial immune systems, and swarm intelligence.

There is a growing interest in applying nature-inspired methods to the analysis of biological data. As a consequence, researchers need to tackle the numerous challenges linked with bioinformatics and neurosciences through analyzing large amounts of heterogeneous complex biological and biomedical data and exploring new applications.

This Special Issue is dedicated to showcasing original research and review articles on nature-inspired methods applied to neurosciences, including bioinformatics. The objective of this Special Issue is to bridge the gap between theoretical researchers and practitioners, enabling the development of nature-inspired methods which meet the demands of real-world applications in various biomedical and neuroscience fields.

Potential topics include but are not limited to the following:

• Nature-inspired computing in evolutionary neuroscience
• Nature-inspired computing in gene expression data and microarrays (including clustering and biclustering)
• Nature-inspired computing in functional magnetic resonance imaging (fMRI) data
• Nature-inspired computing in single nucleotide polymorphism (SNP)
• Nature-inspired computing in comparison and alignment
• Nature-inspired computing in sequence analysis
• Nature-inspired computing in gene selection
• Nature-inspired computing in codon pair context