Combining heterogeneous data sources for accurate functional annotation of proteins.

ABSTRACT: Combining heterogeneous sources of data is essential for accurate prediction of protein function. The task is complicated by the fact that while sequence-based features can be readily compared across species, most other data are species-specific. In this paper, we present a multi-view extension to GOstruct, a structured-output framework for function annotation of proteins. The extended framework can learn from disparate data sources, with each data source provided to the framework in the form of a kernel. Our empirical results demonstrate that the multi-view framework is able to utilize all available information, yielding better performance than sequence-based models trained across species and models trained from collections of data within a given species. This version of GOstruct particip

SUBMITTER: Sokolov A 

PROVIDER: S-EPMC3584846 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature