Drug target inference by mining transcriptional data using a novel graph convolutional network framework
Autor: | Xiaolong Wu, Xiaomin Luo, Xiaozhe Wan, Tianbiao Yang, Xiaohong Liu, Feisheng Zhong, Yinghui Zhang, Kaixian Chen, Sulin Zhang, Dingyan Wang, Xutong Li, Ruirui Yang, Zunyun Fu, Zisheng Fan, Mingyue Zheng, Hualiang Jiang |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Profiling (computer programming)
experimental verification business.industry Deep learning Inference deep learning Proteins Context (language use) Cell Biology Computational biology Gene signature Biochemistry transcriptomics Drug Delivery Systems Drug Discovery Graph (abstract data type) Artificial intelligence business Transcriptome Biomedicine Biological network Biotechnology Research Article drug target inference |
Zdroj: | Protein & Cell |
ISSN: | 1674-8018 1674-800X |
Popis: | A fundamental challenge that arises in biomedicine is the need to characterize compounds in a relevant cellular context in order to reveal potential on-target or off-target effects. Recently, the fast accumulation of gene transcriptional profiling data provides us an unprecedented opportunity to explore the protein targets of chemical compounds from the perspective of cell transcriptomics and RNA biology. Here, we propose a novel Siamese spectral-based graph convolutional network (SSGCN) model for inferring the protein targets of chemical compounds from gene transcriptional profiles. Although the gene signature of a compound perturbation only provides indirect clues of the interacting targets, and the biological networks under different experiment conditions further complicate the situation, the SSGCN model was successfully trained to learn from known compound-target pairs by uncovering the hidden correlations between compound perturbation profiles and gene knockdown profiles. On a benchmark set and a large time-split validation dataset, the model achieved higher target inference accuracy as compared to previous methods such as Connectivity Map. Further experimental validations of prediction results highlight the practical usefulness of SSGCN in either inferring the interacting targets of compound, or reversely, in finding novel inhibitors of a given target of interest. Supplementary Information The online version contains supplementary material available at 10.1007/s13238-021-00885-0. |
Databáze: | OpenAIRE |
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