Journal
BRIEFINGS IN BIOINFORMATICS
Volume 23, Issue 1, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbab364
Keywords
drug combination; network embedding; multiplex network; machine learning
Funding
- National Key Research and Development Program of China [2018YFC0910403]
- National Natural Science Foundation of China [62072353, 61672406]
- Fundamental Research Funds for the Central Universities [QTZX2180]
Ask authors/readers for more resources
In this paper, a Network Embedding frameWork in MultIplex Network (NEWMIN) is proposed to predict synthetic drug combinations. By integrating information from multiple networks and determining their importance, several novel drug combinations have been discovered, with better performance compared to other methods.
Drug combination is a sensible strategy for disease treatment because it improves the treatment efficacy and reduces concomitant side effects. Due to the large number of possible combinations among candidate compounds, exhaustive screening is prohibitive. Currently, a large number of studies have focused on predicting potential drug combinations. However, these methods are not entirely satisfactory in terms of performance and scalability. In this paper, we proposed a Network Embedding frameWork in MultIplex Network (NEWMIN) to predict synthetic drug combinations. Based on a multiplex drug similarity network, we offered alternative methods to integrate useful information from different aspects and to decide the quantitative importance of each network. For drug combination prediction, we found seven novel drug combinations that have been validated by external sources among the top-ranked predictions of our model. To verify the feasibility of NEWMIN, we compared NEWMIN with other five methods, for which it showed better performance than other methods in terms of the area under the precision-recall curve and receiver operating characteristic curve.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available