2020/01/07 by Işıksu Ekşioğlu, Ekşioğlu, Işıksu, Mehmet Tan +1
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computational Drug Discovery Methods #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Metabolomics and Mass Spectrometry Studies #Quantitative Methods (q-bio.QM) #Statistical and Computational Modeling
paper · pdf · doi:10.48550/arxiv.2001.01997
openalex publication_date 2020/01/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.