Drug target inference by mining transcriptional data using a novel graph convolutional network framework.
Drug target inference by mining transcriptional data using a novel graph convolutional network framework.
复制标题
使用新颖的图卷积网络框架通过挖掘转录数据来推断药物靶标
DOI：
10.1007/s13238-021-00885-0
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发表时间：
2022-04
期刊：
影响因子：
21.1
通讯作者：
Zheng M
中科院分区：
文献类型：
--
作者：
Zhong F;Wu X;Yang R;Li X;Wang D;Fu Z;Liu X;Wan X;Yang T;Fan Z;Zhang Y;Luo X;Chen K;Zhang S;Jiang H;Zheng M
生物医学中出现的一个基本挑战是需要在相关的细胞环境中表征化合物，以揭示潜在的靶上或脱靶效应。近年来，基因转录谱数据的快速积累为我们从细胞转录组学和RNA生物学的角度探索化合物的蛋白质靶点提供了前所未有的机会。在这里，我们提出了一种新的基于Siamese光谱的图卷积网络（SSGCN）模型，用于从基因转录谱推断化合物的蛋白质靶点。虽然复合扰动的基因特征只提供了相互作用靶标的间接线索，而且不同实验条件下的生物网络使情况进一步复杂化，但通过揭示复合扰动谱与基因敲低谱之间的隐藏相关性，SSGCN模型成功地从已知的化合物靶标对中学习。在基准集和大型分时验证数据集上，与Connectivity Map等先前的方法相比，该模型实现了更高的目标推理精度。预测结果的进一步实验验证强调了SSGCN在推断化合物的相互作用靶标或相反，在寻找给定靶标的新抑制剂方面的实际用途。在线版本包含补充材料，可在10.1007/s13238-021-00885-0获得。
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. The online version contains supplementary material available at 10.1007/s13238-021-00885-0.
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影响因子：
8.8
作者：
Corrales L;Glickman LH;McWhirter SM;Kanne DB;Sivick KE;Katibah GE;Woo SR;Lemmens E;Banda T;Leong JJ;Metchette K;Dubensky TW Jr;Gajewski TF
通讯作者：
Gajewski TF
DOI：
10.1073/pnas.0708800104
发表时间：
2007-12-18
影响因子：
11.1
作者：
Fedorov, Oleg;Marsden, Brian;Knapp, Stefan
通讯作者：
Knapp, Stefan
影响因子：
11.4
作者：
Braaten, D;Luban, J
通讯作者：
Luban, J
影响因子：
4.3
作者：
Filzen TM;Kutchukian PS;Hermes JD;Li J;Tudor M
通讯作者：
Tudor M
影响因子：
22.7
作者：
Carozza JA;Böhnert V;Nguyen KC;Skariah G;Shaw KE;Brown JA;Rafat M;von Eyben R;Graves EE;Glenn JS;Smith M;Li L
通讯作者：
Li L