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Local Augmented Graph Neural Network for Multi-omics Cancer Prognosis Prediction and Analysis by Yongqing Zhang; Shuwen Xiong; Zixuan Wang; Yuhang Liu; Hong Luo; Beichen Li; Quan Zou is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is Local Augmented Graph Neural Network for Multi-omics Cancer Prognosis Prediction and Analysis about?

Cancer prognosis prediction and analysis can help patients understand expected life and help clinicians provide correct therapeutic guidance. Thanks to the development of sequencing technology, multi-omics data, and biological networks have been used for cancer prognosis prediction. Besides, graph neural networks can simultaneously consider multi-omics features and molecular interactions in biological networks, becoming mainstream in cancer prognosis prediction and analysis. However, the limited number of neighboring genes in biological networks restricts the accuracy of graph neural networks. To solve this problem, a local augmented graph convolutional network named LAGProg is proposed in this paper for cancer prognosis prediction and analysis. The process follows: first, given a patient's multi-omics data features and biological network, the corresponding augmented conditional variational autoencoder generates features. Then, the generated augmented features and the original features are fed into a cancer prognosis prediction model to complete the cancer prognosis prediction task. The conditional variational autoencoder consists of two parts: encoder-decoder. In the encoding phas

Who reads Local Augmented Graph Neural Network for Multi-omics Cancer Prognosis Prediction and Analysis?

Author

Yongqing Zhang; Shuwen Xiong; Zixuan Wang; Yuhang Liu; Hong Luo; Beichen Li; Quan Zou

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)