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Dual-Stream Heterogeneous Graph Neural Network Based on Zero-Shot Embeddings for Predicting miRNA-Drug Sensitivity

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成果类型:
会议论文
作者:
Li Peng;Wang Wang;Cheng Yang;Wenhui Xiao;Xiangzheng Fu;...
作者机构:
[Li Peng; Wang Wang; Cheng Yang; Wenhui Xiao] College of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, China
[Yifan Chen] College of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha, China
[Xiangzheng Fu] College of Chinese Medicine, Hong Kong Baptist University, Hong Kong, SAR China
语种:
英文
关键词:
miRNA-drug sensitivity;graph neural network;zero-shot embeddings;large language models
年:
2024
页码:
1122-1128
会议名称:
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议论文集名称:
2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议时间:
03 December 2024
会议地点:
Lisbon, Portugal
出版者:
IEEE
ISBN:
979-8-3503-8623-3
基金类别:
10.13039/501100001809-National Natural Science Foundation of China
机构署名:
本校为其他机构
院系归属:
计算机与信息工程学院
摘要:
MicroRNAs (miRNAs) are a class of non-coding RNA molecules that have been shown to be closely associated with the sensitivity of chemotherapeutic drugs in cancer treatment. Given the high cost and extended duration of traditional biological experiments, there is an urgent need to develop computational models to predict the sensitivity scores between miRNAs and drugs. In this study, we proposed a dual-stream graph neural network method based on Zero-Shot Embeddings, named DSHGZS, to explore the potential sensitivity scores between miRNAs and dru...

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