The following publications are possibly variants of this publication:
- HetBiSyn: Predicting Anticancer Synergistic Drug Combinations Featuring Bi-perspective Drug Embedding with Heterogeneous DataYulong Li, Hongming Zhu, Xiaowen Wang, Qin Liu. isbra 2023: 464-475 [doi]
- Predicting anticancer synergistic drug combinations based on multi-task learningDanYi Chen, Xiaowen Wang, Hongming Zhu, Yizhi Jiang, Yulong Li, Qi Liu, Qin Liu. bmcbi, 24(1):448, December 2023. [doi]
- TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samplesLiye He, Krister Wennerberg, Tero Aittokallio, Jing Tang. bioinformatics, 31(11):1866-1868, 2015. [doi]
- DTSyn: a dual-transformer-based neural network to predict synergistic drug combinationsJing Hu, Jie Gao, Xiaomin Fang, Zijing Liu, Fan Wang, Weili Huang, Hua Wu 0003, Guodong Zhao. bib, 23(5), 2022. [doi]
- Trustworthy Deep Neural Network for Inferring Anticancer Synergistic CombinationsMuhammad Alsherbiny, Ibrahim Radwan, Nour Moustafa, Deep Jyoti Bhuyan, Muath El-Waisi, Dennis Chang, Chunguang Li. titb, 27(4):1691-1700, April 2023. [doi]
- Predicting Tumor Cell Response to Synergistic Drug Combinations Using a Novel Simplified Deep Learning ModelHeming Zhang, Jiarui Feng, Amanda Zeng, Philip R. O. Payne, Fuhai Li. amia 2020: [doi]
- Predicting effective drug combinations using gradient tree boosting based on features extracted from drug-protein heterogeneous networkHui Liu, Wenhao Zhang, Lixia Nie, Xiancheng Ding, Judong Luo, Ling Zou 0002. bmcbi, 20(1):645, 2019. [doi]