Biological data mining purports to extract useful knowledge from massive datasets gathered in biology, and in other related life sciences areas such as medicine and neuroscience. In the recent past, we have witnessed major transformations of these applied sciences into data-driven endeavors. For instance, the study of even a focused aspect of cellular activity, such as gene action, now benefits from multiple highthroughput data acquisition technologies such as microarrays, genome-wide deletion screens, and RNAi assays. Consequently, analysis and mining techniques, especially those that provide data reduction down to manageable quantities, have become a mainstay of these application domains. This special issue presents novel research in biological data mining applications. The selected submissions went through two rounds of reviews by at least three reviewers. We are very grateful to the anonymous reviewers in helping us select the following papers for this special issue. The first paper, Semi-supervised learning for classification of protein sequence data, by Brian King and Chittibabu Guda, presents a com-