GSDRP: Fusing Drug Sequence Features with Graph Features to Predict Drug Response
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5128-0_13
Reference32 articles.
1. Partin, A., Brettin, T.S., Zhu, Y., et al.: Deep learning methods for drug response prediction in cancer: predominant and emerging trends. Front. Med. 10, 1086097 (2023)
2. Chen, S., Yang, Y., Zhou, H., et al.: DNN-PNN: a parallel deep neural network model to improve anticancer drug sensitivity. Methods 209, 1–9 (2023)
3. Peng, W., Chen, T., Liu, H., et al.: Improving drug response prediction based on two-space graph convolution. Comput. Biol. Med. 158, 106859 (2023)
4. Wang, H., Dai, C., Wen, Y., et al.: GADRP: graph convolutional networks and autoencoders for cancer drug response prediction. Brief. Bioinform. 24(1), bbac501 (2023)
5. Jiang, L., Jiang, C., Yu, X., et al.: DeepTTA: a transformer-based model for predicting cancer drug response. Brief. Bioinform. 23(3), bbac100 (2022)
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