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Authors: Mingyuan Xin ; Jun Fan and Zhenran Jiang

Affiliation: East China Normal University, China

Keyword(s): Drug-pathway Interaction, Ensemble Learning, AdaBoost, Bagging, Random SubSpace.

Abstract: Recently, developing computational methods to explore drug-pathway interaction relationships has attracted attention for their potentiality in discovering unknown targets and mechanisms of drug actions. However, mining suitable features of drugs and pathways is challenging for available prediction methods. This paper performed an ensemble learning-based method to predict potential drug-pathway interactions by integrating different drug-based and pathway-based features. The main characteristic of our method lies in using the Relief algorithm for feature selection and regarding three ensemble methods (AdaBoost, Bagging and Random Subspace) for classifiers. Cross validation results showed the AdaBoost algorithm that based on the Decision Tree classifier can obtain a higher prediction accuracy, which indicated the effectiveness of ensemble learning. Moreover, some new predicted interactions were validated by database searching, which demonstrated its potentiality for further biological e xperiment investigation. (More)

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Paper citation in several formats:
Xin, M.; Fan, J. and Jiang, Z. (2017). Ensemble Learning-based Prediction of Drug-pathway Interactions based on Features Integration. In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOINFORMATICS; ISBN 978-989-758-214-1; ISSN 2184-4305, SciTePress, pages 117-124. DOI: 10.5220/0006096701170124

@conference{bioinformatics17,
author={Mingyuan Xin. and Jun Fan. and Zhenran Jiang.},
title={Ensemble Learning-based Prediction of Drug-pathway Interactions based on Features Integration},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOINFORMATICS},
year={2017},
pages={117-124},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006096701170124},
isbn={978-989-758-214-1},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2017) - BIOINFORMATICS
TI - Ensemble Learning-based Prediction of Drug-pathway Interactions based on Features Integration
SN - 978-989-758-214-1
IS - 2184-4305
AU - Xin, M.
AU - Fan, J.
AU - Jiang, Z.
PY - 2017
SP - 117
EP - 124
DO - 10.5220/0006096701170124
PB - SciTePress

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