Prediction of Drug Synergy by Ensemble Learning

Autor:	Ekşioğlu, Işıksu, Tan, Mehmet
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Quantitative Biology - Quantitative Methods Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	One of the promising methods for the treatment of complex diseases such as cancer is combinational therapy. Due to the combinatorial complexity, machine learning models can be useful in this field, where significant improvements have recently been achieved in determination of synergistic combinations. In this study, we investigate the effectiveness of different compound representations in predicting the drug synergy. On a large drug combination screen dataset, we first demonstrate the use of a promising representation that has not been used for this problem before, then we propose an ensemble on representation-model combinations that outperform each of the baseline models. Comment: Appeared in Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB) 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.01997 Zobrazit plný text záznamu View this record from Arxiv