Towards the prediction of essential genes by integration of network topology, cellular localization and biological process information |
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Authors: | Marcio L Acencio Ney Lemke |
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Institution: | 1. Department of Physics and Biophysics, S?o Paulo State University, Distrito de Rubiao Jr. s/n, Botucatu, S?o Paulo, Brazil
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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. |
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Keywords: | |
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