AutoGGN: A gene graph network AutoML tool for multi-omics research
文献类型：期刊论文
| 作者 | Zhang, Lei1; Shen, Wen1; Li, Ping2; Xu, Chi1; Liu, Denghui1; He, Wenjun1; Xu, Zhimeng1; Wang, Deyong1; Zhang, Chenyi2; Jiang, Hualiang3
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| 刊名 | Artificial Intelligence in the Life Sciences
 |
| 出版日期 | 2021-11-29 |
| 卷号 | 1页码:100019 |
| 关键词 | Multi-omics Data Molecular Interaction Network Graph Convolution Network Deep Learning Single-Cell Stage Classification Cancer Type Classification Cancer Subtyping |
| ISSN号 | 2667-3185 |
| DOI | 10.1016/j.ailsci.2021.100019 |
| 英文摘要 | Omics data can be used to identify biological characteristics from genetic to phenotypic levels during the life span of a living being, while molecular interaction networks have a fundamental impact on life activities. Integrating omics data and molecular interaction networks will help researchers delve into comprehensive information hidden in the data. Here, we propose a new multimodal method — AutoGGN — to integrate multi-omics data with molecular interaction networks based on graph convolutional neural networks (GCNs). We evaluated AutoGGN using three classification tasks: single-cell embryonic developmental stage classification, pan-cancer type classification, and breast cancer subtyping. On all three tasks, AutoGGN showed better performance than other methods. This means AutoGGN has the potential to extract insights more effectively by means of integrating molecular interaction networks with multi-omics data. Additionally, in order to provide a better understanding of how our model makes predictions, we utilized the SHAP module and identified the key genes contributing to the classification, providing insight for the design of downstream biological experiments. |
| 语种 | 英语 |
| 源URL | [http://119.78.100.183/handle/2S10ELR8/309260]  |
| 专题 | 新药研究国家重点实验室 |
| 通讯作者 | Qiao, Nan |
| 作者单位 | 1.Lab of Health Intelligence, Huawei Technologies Co. Ltd.; 2.Graph Engine Service, Huawei Technologies Co. Ltd.; 3.Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences |
| 推荐引用方式 GB/T 7714 | Zhang, Lei,Shen, Wen,Li, Ping,et al. AutoGGN: A gene graph network AutoML tool for multi-omics research[J]. Artificial Intelligence in the Life Sciences,2021,1:100019. |
| APA | Zhang, Lei.,Shen, Wen.,Li, Ping.,Xu, Chi.,Liu, Denghui.,...&Qiao, Nan.(2021).AutoGGN: A gene graph network AutoML tool for multi-omics research.Artificial Intelligence in the Life Sciences,1,100019. |
| MLA | Zhang, Lei,et al."AutoGGN: A gene graph network AutoML tool for multi-omics research".Artificial Intelligence in the Life Sciences 1(2021):100019. |
入库方式： OAI收割
来源：上海药物研究所
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