Predicting Synthetic Lethality in Human Cancers via Knowledge Graph Summarization
Author: 
Yunfei WuAuthors Info & Claims
Yunfei WuAuthors Info & ClaimsPages 176 - 181
Abstract
Synthetic lethality (SL) is of great significance for the development of anticancer drugs. Thanks to the increasing richness of biomedical knowledge graph (KG), machine learning algorithm combined with knowledge graph has been used for SL prediction and achieved good performance. However, how to effectively utilize biomedical KG to learn stronger gene feature representation is an open problem. Therefore, a self-attention based graph summarization scheme is introduced to delete unimportant nodes and edges in the graph. Firstly, the knowledge graph is used to construct the subgraph of the target gene node, and then the self-attention mechanism is used to calculate the edge signal intensities score in the subgraph, so as to remove the entities that are not important to the target gene in the subgraph. Finally, latent features of gene node are learned by graph neural network for SL prediction. The experimental findings demonstrate that our method performs better than the best baseline method, demonstrating the viability of our method.
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February 2023
398 pages
ISBN:9798400700200
DOI:10.1145/3592686
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Published: 31 May 2023
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BIC 2023
BIC 2023: 2023 3rd International Conference on Bioinformatics and Intelligent Computing
February 10 - 12, 2023
Sanya, China
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