Anti-cancer Drug Activity Prediction by Ensemble Learning

Ertan Tolan, Mehmet Tan

Abstract

Personalized cancer treatment is an ever-evolving approach due to complexity of cancer. As a part of personalized therapy, effectiveness of a drug on a cell line is measured. However, these experiments are backbreaking and money consuming. To surmount these difficulties, computational methods are used with the provided data sets. In the present study, we considered this as a regression problem and designed an ensemble model by combining three different regression models to reduce prediction error for each drug-cell line pair. Two major data sets, were used to evaluate our method. Results of this evaluation show that predictions of ensemble method are significantly better than models \emph{per se}. Furthermore, we report the cytotoxicty predictions of our model for the drug-cell line pairs that do not appear in the original data sets.

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Paper Citation


in Harvard Style

Tolan E. and Tan M. (2016). Anti-cancer Drug Activity Prediction by Ensemble Learning . In Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016) ISBN 978-989-758-203-5, pages 431-436. DOI: 10.5220/0006085704310436


in Bibtex Style

@conference{kdir16,
author={Ertan Tolan and Mehmet Tan},
title={Anti-cancer Drug Activity Prediction by Ensemble Learning},
booktitle={Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)},
year={2016},
pages={431-436},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006085704310436},
isbn={978-989-758-203-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - Volume 1: KDIR, (IC3K 2016)
TI - Anti-cancer Drug Activity Prediction by Ensemble Learning
SN - 978-989-758-203-5
AU - Tolan E.
AU - Tan M.
PY - 2016
SP - 431
EP - 436
DO - 10.5220/0006085704310436