Prediction of Essential Genes based on Machine Learning and Information Theoretic Features

Dawit Nigatu, Werner Henkel

Abstract

Computational tools have enabled a relatively simple prediction of essential genes (EGs), which would otherwise be done by costly and tedious gene knockout experimental procedures. We present a machine learning based predictor using information-theoretic features derived exclusively from DNA sequences. We used entropy, mutual information, conditional mutual information, and Markov chain models as features. We employed a support vector machine (SVM) classifier and predicted the EGs in 15 prokaryotic genomes. A fivefold cross-validation on the bacteria E. coli, B. subtilis, and M. pulmonis resulted in AUC score of 0.85, 0.81, and 0.89, respectively. In cross-organism prediction, the EGs of a given bacterium are predicted using a model trained on the rest of the bacteria. AUC scores ranging from 0.66 to 0.9 and averaging 0.8 were obtained. The average AUC of the classifier on a one-to-one prediction among E. coli, B. subtilis, and Acinetobacter is 0.85. The performance of our predictor is comparable with recent and state-of-the art predictors. Considering that we used only sequence information on a problem that is much more complicated, the achieved results are very good.

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Paper Citation


in Harvard Style

Nigatu D. and Henkel W. (2017). Prediction of Essential Genes based on Machine Learning and Information Theoretic Features . In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017) ISBN 978-989-758-214-1, pages 81-92. DOI: 10.5220/0006165700810092


in Bibtex Style

@conference{bioinformatics17,
author={Dawit Nigatu and Werner Henkel},
title={Prediction of Essential Genes based on Machine Learning and Information Theoretic Features},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)},
year={2017},
pages={81-92},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006165700810092},
isbn={978-989-758-214-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)
TI - Prediction of Essential Genes based on Machine Learning and Information Theoretic Features
SN - 978-989-758-214-1
AU - Nigatu D.
AU - Henkel W.
PY - 2017
SP - 81
EP - 92
DO - 10.5220/0006165700810092