Abstract
Many traditional drug prediction models mostly focus on analyzing single omics data, while ignoring the rich multi-omics data in bioinformatics. Moreover, they failed to make full use of the drug complementary information of sequence features and graphical features when considering the SMILES(Simplified molecular input line entry system) features. In view of this, we propose a deep learning model GSDRP that effectively integrates omics data and drug attribute information. SA-BiLSTM is used to extract one-dimensional sequence features of drugs, and Graph Transformer and GAT_GCN capture two-dimensional structural features, which are then fused by the graph sequence attention module. At the same time, the omics data features are processed by convolutional neural network. Finally, the cross-attention module in GSDRP facilitates the fusion of omics and drug features to enhance interactions for better prediction. Experiments on the Cancer Drug Sensitivity Database (GDSC) show that GSDRP can effectively combine multi-omics information such as genome aberration (MUT_CNA) and gene expression (GE) with the 1-D and 2-D features of SMILES to significantly improve the accuracy of drug response prediction. The comparison results with four other state-of-the-art methods further demonstrate the superior performance of GSDRP in drug response prediction. In addtion,we identify important omics markers and important characteristics of drugs that affect HCC celllines response prediction. This will not only help to understand the therapeutic mechanism of hepatocellular carcinoma, but also provide strong support for future individualized treatment.
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Acknowledgments
This research was supported by the Joint Fund for the Innovation of Science and Technology of Fujian Province (2021Y9197), School Management Project of Fujian University of Traditional Chinese Medicine (XB2023185), and the Natural Science Foundation of Fujian Province (2023J011262).
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Peng, X., Dang, Y., Huang, J., Luo, S., Xiong, Z. (2024). GSDRP: Fusing Drug Sequence Features with Graph Features to Predict Drug Response. In: Peng, W., Cai, Z., Skums, P. (eds) Bioinformatics Research and Applications. ISBRA 2024. Lecture Notes in Computer Science(), vol 14954. Springer, Singapore. https://doi.org/10.1007/978-981-97-5128-0_13
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DOI: https://doi.org/10.1007/978-981-97-5128-0_13
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