In silico network topology-based prediction of gene essentiality

João Paulo Müller da Silva, Marcio Luis Acencio, José Carlos Merino Mombach, Renata Vieira, José Camargo da Silva, Ney Lemke and Marialva Sinigaglia

Physica A: Statistical Mechanics and its Applications, 2008, vol. 387, issue 4, 1049-1055

Abstract: The identification of genes essential for survival is important for the understanding of the minimal requirements for cellular life and for drug design. As experimental studies with the purpose of building a catalog of essential genes for a given organism are time-consuming and laborious, a computational approach which could predict gene essentiality with high accuracy would be of great value. We present here a novel computational approach, called NTPGE (Network Topology-based Prediction of Gene Essentiality), that relies on the network topology features of a gene to estimate its essentiality. The first step of NTPGE is to construct the integrated molecular network for a given organism comprising protein physical, metabolic and transcriptional regulation interactions. The second step consists in training a decision-tree-based machine-learning algorithm on known essential and non-essential genes of the organism of interest, considering as learning attributes the network topology information for each of these genes. Finally, the decision-tree classifier generated is applied to the set of genes of this organism to estimate essentiality for each gene. We applied the NTPGE approach for discovering the essential genes in Escherichia coli and then assessed its performance.

Keywords: Biological networks; Complex systems; Gene essentiality; Machine learning (search for similar items in EconPapers)
Date: 2008
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:387:y:2008:i:4:p:1049-1055

DOI: 10.1016/j.physa.2007.10.044

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