Engineering support vector machine kernels that recognize translation initiation sites.
BACKGROUND
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.
Zien
A
A
GMD.SCAI, Schloss Birlinghoven, 53754 Sankt Augustin, Germany. Alexander.Zien@gmd.de
Rätsch
G
G
Mika
S
S
Schölkopf
B
B
Lengauer
T
T
Müller
K R
KR
D000465:Algorithms; D000818:Animals; D018387:Codon, Initiator / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D023281:Genomics / Q000379:methods*; D006801:Humans; D011237:Predictive Value of Tests; D014176:Protein Biosynthesis / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods*; D014714:Vertebrates / Q000235:genetics
D018387:Codon, Initiator