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A Review of Support Vector Machines in Computational Biology

In the biological sciences, arguably moreso than in any other discipline, the amount of data is available to researchers is exploding exponentially. Making this information available in a consistent, accessible format is itself a non-trivial task, and categorizing or classifying the data in meaningful ways is especially daunting. Laboratory experiment and human review will likely continue to represent the gold standard for reliability in these classification tasks, but these processes are time-consuming and expensive. Consequently, the development of automated replacements for these is an extremely important and widely-studied problem. Due to the scope and complexity of the data in question, statistical and machine learning algorithms are a natural choice for these types of analyses. Support vector machines are a specific type of machine learning algorithm that are among the most widely-used for many statistical learning problems, such as spam filtering, text classification, handwriting analysis, face and object recognition, and countless others. Support vector machines have also come into widespread use in practically every area of bioinformatics within the last ten years, and their area of influence continues to expand today. This paper will present a brief description of support vector machines themselves, followed by a comprehensive study of how SVMs have been and are being used in computational biology, and finally a brief discussion of some novel variations on SVM classification as well as the future of SVMs in bioinformatics.