Think globally and solve locally: secondary memory-based network learning for automated multi-species function prediction.

ABSTRACT:

Background

Network-based learning algorithms for automated function prediction (AFP) are negatively affected by the limited coverage of experimental data and limited a priori known functional annotations. As a consequence their application to model organisms is often restricted to well characterized biological processes and pathways, and their effectiveness with poorly annotated species is relatively limited. A possible solution to this problem might consist in the construction of big networks including multiple species, but this in turn poses challenging computational problems, due to the scalability limitations of existing algorithms and the main memory requirements induced by the construction of big networks. Distributed computation or the usage of big computers could in princip

SUBMITTER: Mesiti M 

PROVIDER: S-EPMC4006453 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature