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Publications

In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data

Published in Scientific Reports, 2019

We presented our method for DREAM AstraZeneca-Sanger Drug Combination Prediction Challenge to predict synergistic drug combinations. In this paper, we show that our machine learning model obtained the primary metric = 0.36 and the tie-breaker metric = 0.37 in the extension round of the challenge which was ranked in top 15 out of 76 submissions. In addition, we analyzed our model’s predictions to better understand the molecular processes underlying synergy and discovered that key regulators of tumorigenesis such as TNFA and BRAF are often targets in synergistic interactions, while MYC is often duplicated. We ultimately presented novel hypotheses for synergistic drug pairs and their mechanisms that are supported by both computational predictions and biological understanding.

Recommended citation: Celebi, R., Bear Don’t Walk, O., Movva, R. et al. In-silico Prediction of Synergistic Anti-Cancer Drug Combinations Using Multi-omics Data. Sci Rep 9, 8949 (2019). https://doi.org/10.1038/s41598-019-45236-6 https://www.nature.com/articles/s41598-019-45236-6.pdf

Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings

Published in BMC Bioinformatics, 2009

We present realistic evaluation settings to predict drug-drug interactions (DDIs) using knowledge graph embeddings. Methods for prediction of DDIs have tendency to report high accuracy but yet there has been little impact on translational research due to systematic biases induced by networked/paired data. In the study, we propose a simple disjoint cross-validation scheme to evaluate drug-drug interaction predictions for the scenarios where the drugs have no known DDIs. Our contribution can be summarized as follows : i) comparison of different knowledge graph embedding approaches on DDI prediction task ii) evaluation of different knowledge graphs as background knowledge for feature learning iii) testing DDI prediction task for the cold-start scenarios.

Recommended citation: Celebi, R., Uyar, H., Yasar, E. et al. Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction in realistic settings. BMC Bioinformatics 20, 726 (2019). https://doi.org/10.1186/s12859-019-3284-5 https://bmcbioinformatics.biomedcentral.com/track/pdf/10.1186/s12859-019-3284-5.pdf