101. Prediction of drug response in multilayer networks based on fusion of multiomics data.
- Author
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Yu, Liang, Zhou, Dandan, Gao, Lin, and Zha, Yunhong
- Subjects
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DRUG target , *GENE expression profiling , *GENETIC mutation , *DATA fusion (Statistics) , *SOMATIC mutation , *INDIVIDUALIZED medicine - Abstract
• Individual response to drugs is crucial for personalized medicine. • We developed DREMO for predicting drug response in individuals. • 3 cell line similarity networks and 2 drug similarity networks were constructed. • The similarity information networks were fused with low-dimensional feature vectors. • DREMO addresses the "small n, large p" issue to a certain extent. Predicting the response of each individual patient to a drug is a key issue assailing personalized medicine. Our study predicted drug response based on the fusion of multiomics data with low-dimensional feature vector representation on a multilayer network model. We named this new method DREMO (D rug R esponse pr E diction based on M ulti O mics data fusion). DREMO fuses similarities between cell lines and similarities between drugs, thereby improving the ability to predict the response of cancer cell lines to therapeutic agents. First, a multilayer similarity network related to cell lines and drugs was constructed based on gene expression profiles, somatic mutation, copy number variation (CNV), drug chemical structures, and drug targets. Next, low-dimensional feature vector representation was used to fuse the biological information in the multilayer network. Then, a machine learning model was applied to predict new drug-cell line associations. Finally, our results were validated using the well-established GDSC/CCLE databases, literature, and the functional pathway database. Furthermore, a comparison was made between DREMO and other methods. Results of the comparison showed that DREMO improves predictive capabilities significantly. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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