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Prediction of essential proteins using topological properties in GO-pruned PPI network based on machine learning methods

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1 Author(s)
Wooyoung Kim ; Department of Computing and Software Systems, University of Washington Bothell, Bothell, WA 98011, USA

The prediction of essential proteins, the minimal set required for a living cell to support cellular life, is an important task to understand the cellular processes of an organism. Fast progress in high-throughput technologies and the production of large amounts of data enable the discovery of essential proteins at the system level by analyzing Protein-Protein Interaction (PPI) networks, and replacing biological or chemical experiments. Furthermore, additional gene-level annotation information, such as Gene Ontology (GO) terms, helps to detect essential proteins with higher accuracy. Various centrality algorithms have been used to determine essential proteins in a PPI network, and, recently motif centrality GO, which is based on network motifs and GO terms, works best in detecting essential proteins in a Baker's yeast Saccharomyces cerevisiae PPI network, compared to other centrality algorithms. However, each centrality algorithm contributes to the detection of essential proteins with different properties, which makes the integration of them a logical next step. In this paper, we construct a new feature space, named CENT-ING-GO consisting of various centrality measures and GO terms, and provide a computational approach to predict essential proteins with various machine learning techniques. The experimental results show that CENT-ING-GO feature space improves performance over the INT-GO feature space in previous work by Acencio and Lemke in 2009. We also demonstrate that pruning a PPI with informative GO terms can improve the prediction performance further.

Published in:

Tsinghua Science and Technology  (Volume:17 ,  Issue: 6 )