Back
Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites in DNA
In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points from which regions encoding pro teins start, the socalled translation initiation sites (TIS). This can be modeled as a classification prob lem. We demonstrate the power of support vector machines (SVMs) for this task, and show how to suc cessfully incorporate biological prior knowledge by engineering an appropriate kernel function.
@inproceedings{5046, title = {Engineering Support Vector Machine Kernels That Recognize Translation Initiation Sites in DNA}, booktitle = {German Conference on Bioinformatics (GCB 1999)}, abstract = {In order to extract protein sequences from nucleotide sequences, it is an important step to recognize points from which regions encoding pro teins start, the socalled translation initiation sites (TIS). This can be modeled as a classification prob lem. We demonstrate the power of support vector machines (SVMs) for this task, and show how to suc cessfully incorporate biological prior knowledge by engineering an appropriate kernel function. }, organization = {Max-Planck-Gesellschaft}, month = oct, year = {1999}, slug = {5046}, author = {Zien, A. and R{\"a}tsch, G. and Mika, S. and Sch{\"o}lkopf, B. and Lemmen, C. and Smola, A. and Lengauer, T. and M{\"u}ller, K-R.}, month_numeric = {10} }