MOTIVATION: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code 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 provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.
MOTIVATION: In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points at which regions start that code 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 provide evidence that existing related methods (e.g. ESTScan) could profit from advanced TIS recognition.
Authors: Gabriele Schweikert; Alexander Zien; Georg Zeller; Jonas Behr; Christoph Dieterich; Cheng Soon Ong; Petra Philips; Fabio De Bona; Lisa Hartmann; Anja Bohlen; Nina Krüger; Sören Sonnenburg; Gunnar Rätsch Journal: Genome Res Date: 2009-06-29 Impact factor: 9.043
Authors: Xiang H-F Zhang; Katherine A Heller; Ilana Hefter; Christina S Leslie; Lawrence A Chasin Journal: Genome Res Date: 2003-12 Impact factor: 9.043
Authors: Tobias Mann; Richard Humbert; Michael Dorschner; John Stamatoyannopoulos; William Stafford Noble Journal: Nucleic Acids Res Date: 2009-06-15 Impact factor: 16.971