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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

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

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Author(s):
Acencio, Marcio L. [1] ; Lemke, Ney [1]
Total Authors: 2
Affiliation:
[1] Sao Paulo State Univ, Dept Phys & Biophys, Sao Paulo - Brazil
Total Affiliations: 1
Document type: Journal article
Source: BMC Bioinformatics; v. 10, SEP 16 2009.
Web of Science Citations: 93
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 features, cellular compartments and biological processes-to generate various predictors of essential genes. We showed that the predictors with better performances are those generated by datasets with integrated attributes. Using the predictor with all attributes, i.e., network topological features, cellular compartments and biological processes, we obtained the best predictor of essential genes that was then used to classify yeast genes with unknown essentiality status. Finally, we generated decision trees by training the J48 algorithm on datasets with all network topological features, cellular localization and biological process information to discover cellular rules for essentiality. We found that the number of protein physical interactions, the nuclear localization of proteins and the number of regulating transcription factors are the most important factors determining gene essentiality. Conclusion: We were able to demonstrate that network topological features, cellular localization and biological process information are reliable predictors of essential genes. Moreover, by constructing decision trees based on these data, we could discover cellular rules governing essentiality. (AU)

FAPESP's process: 07/02827-9 - Characterization and simulation of biological networks
Grantee:Ney Lemke
Support Opportunities: Regular Research Grants
FAPESP's process: 07/01213-7 - In silico construction of integrated molecular networks encompassing cell cycle regulation by extracellular matrix adhesion in Homo sapiens
Grantee:Marcio Luis Acencio
Support Opportunities: Scholarships in Brazil - Doctorate