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Abstract: The identification of genes essential for survival is important for theunderstanding of the minimal requirements for cellular life and for drugdesign. As experimental studies with the purpose of building a catalog ofessential genes for a given organism are time-consuming and laborious, acomputational approach which could predict gene essentiality with high accuracywould be of great value. We present here a novel computational approach, calledNTPGE Network Topology-based Prediction of Gene Essentiality, that relies onnetwork topology features of a gene to estimate its essentiality. The firststep of NTPGE is to construct the integrated molecular network for a givenorganism comprising protein physical, metabolic and transcriptional regulationinteractions. The second step consists in training a decision tree-basedmachine learning algorithm on known essential and non-essential genes of theorganism of interest, considering as learning attributes the network topologyinformation for each of these genes. Finally, the decision tree classifiergenerated is applied to the set of genes of this organism to estimateessentiality for each gene. We applied the NTPGE approach for discoveringessential genes in Escherichia coli and then assessed its performance.

Autor: Joao Paulo Muller da Silva, Marcio Luis Acencio, Jose Carlos Merino Mombach, Renata Vieira, Jose Guliherme Camargo da Silva, Ney


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