Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information.

ABSTRACT:

Background

The identification of essential genes is important for the understanding of the minimal requirements for cellular life and for practical purposes, such as drug design. However, the experimental techniques for essential genes discovery are labor-intensive and time-consuming. Considering these experimental constraints, a computational approach capable of accurately predicting essential genes would be of great value. We therefore present here a machine learning-based computational approach relying on network topological features, cellular localization and biological process information for prediction of essential genes.

Results

We constructed a decision tree-based meta-classifier and trained it on datasets with individual and grouped attributes-network topological fe

SUBMITTER: Acencio ML 

PROVIDER: S-EPMC2753850 | biostudies-literature | 2009 Sep

REPOSITORIES: biostudies-literature