Engineering support vector machine kernels that recognize translation initiation sites

Motivation: In order to extract protein sequences from nucleotide sequences , it is an important step to recognize points at which regions start that c ode for proteins. These points are called translation initiation sites (TIS ). Results: The task of finding TIS can be modeled as a classification problem . We demonstrate the applicability of support vector machines for this task , and show how to incorporate prior biological knowledge by engineering an appropriate kernel function. With the described techniques the recognition performance can be improved by 26% over leading existing approaches. We pro vide evidence that existing related methods (e.g. ESTScan) could profit fro m advanced TIS recognition.