Bioinformatics has become increasingly useful in science and industry, but the limitations of traditional algorithms have made modeling difficult. At the same time, researchers have made significant advances in computational intelligence, and it appears from these 13 articles their work could significantly ease the current bottleneck. Papers address gene expression analysis and systems biology (including such topics as neural classifier and swarm intelligence in multi-class cancer diagnosis, gene expression profiles and evolutionary computation, clusters in gene expression data, and the application of evolutionary computing to gene networks), sequence analysis and feature detection (including fuzzy-granular models for identification of marker genes, evolutionary feature selection, fuzzy approaches to the analysis of CpG island methylation patterns), molecular structure and phylogenetics (including evolutionary algorithms in a variety of applications and machine learning approaches for prediction of human mitochondrial proteins), and medicine (featuring evolutionary algorithms for chemotherapy and fuzzy ontology text mining of biomedical texts).