On the String Kernel Pre-Image Problem with Applications in Drug Discovery
Abstract
The pre-image problem has to be solved during inference by most structured output predictors. For string kernels, this problem corresponds to finding the string associated to a given input. An algorithm capable of solving or finding good approximations to this problem would have many applications in computational biology and other fields. This work uses a recent result on combinatorial optimization of linear predictors based on string kernels to develop, for the pre-image, a low complexity upper bound valid for many string kernels. This upper bound is used with success in a branch and bound searching algorithm. Applications and results in the discovery of druggable peptides are presented and discussed.
Cite
@article{arxiv.1412.1463,
title = {On the String Kernel Pre-Image Problem with Applications in Drug Discovery},
author = {Sébastien Giguère and Amélie Rolland and François Laviolette and Mario Marchand},
journal= {arXiv preprint arXiv:1412.1463},
year = {2014}
}
Comments
Peer-reviewed and accepted for presentation at Machine Learning in Computational Biology 2014, Montr\'eal, Qu\'ebec, Canada