An empirical study of supervised learning for biological sequence profiling and microarray expression data analysis

Recent years have seen increasing quantities of high-throughput biological data available for genetic disease profiling, protein structure and function prediction, and new drug and therapy discovery. High-throughput biological experiments output high volume and/or high dimensional data, which impose significant challenges for molecular biologists and domain experts to properly and rapidly digest and interpret the data. In this paper, we provide simple background knowledge for computer scientists to understand how supervised learning tools can be used to solve biological challenges, with a primary focus on two types of problems: Biological sequence profiling and microarray expression data analysis. We employ a set of supervised learning methods to analyze four types of biological data: (1) gene promoter site prediction; (2) splice junction prediction; (3) protein structure prediction; and (4) gene expression data analysis. We argue that although existing studies favor one or two learning methods (such as Support Vector Machines), such conclusions might have been biased, mainly because of the inadequacy of the measures employed in their study. A line of learning algorithms should be considered in different scenarios, depending on the objective and the requirement of the applications, such as the system running time or the prediction accuracy on the minority class examples.