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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 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.

Author(s): Zien, A. and Rätsch, G. and Mika, S. and Schölkopf, B. and Lengauer, T. and Müller, K-R.
Journal: Bioinformatics
Volume: 16
Number (issue): 9
Pages: 799-807
Year: 2000
Month: September
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1093/bioinformatics/16.9.799
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: Web


  title = {Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites},
  author = {Zien, A. and R{\"a}tsch, G. and Mika, S. and Sch{\"o}lkopf, B. and Lengauer, T. and M{\"u}ller, K-R.},
  journal = {Bioinformatics},
  volume = {16},
  number = {9},
  pages = {799-807},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = sep,
  year = {2000},
  month_numeric = {9}