2007/09/26 by João Paulo Müller da Silva, Joao Paulo Muller da Silva, Márcio Luís Acencio +14
Biochemistry, Genetics and Molecular Biology · Physics and Astronomy · #Bioinformatics and Genomic Networks #Biological Physics (physics.bio-ph) #Biomedical Text Mining and Ontologies #FOS: Biological sciences #FOS: Physical sciences #Genomics (q-bio.GN) #Machine Learning in Bioinformatics #physics.bio-ph #q-bio.GN
paper · pdf · doi:10.48550/arxiv.0709.4206
arxiv created 2007/09/26 · openalex publication_date 2007/09/26 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 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 essential genes in Escherichia coli and then assessed its performance.