Protein fold recognition using geometric kernel data fusion.

ABSTRACT:

Motivation

Various approaches based on features extracted from protein sequences and often machine learning methods have been used in the prediction of protein folds. Finding an efficient technique for integrating these different protein features has received increasing attention. In particular, kernel methods are an interesting class of techniques for integrating heterogeneous data. Various methods have been proposed to fuse multiple kernels. Most techniques for multiple kernel learning focus on learning a convex linear combination of base kernels. In addition to the limitation of linear combinations, working with such approaches could cause a loss of potentially useful information.

Results

We design several techniques to combine kernel matrices by taking more involved, geo

SUBMITTER: Zakeri P 

PROVIDER: S-EPMC4071197 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature