4.7 Article

Exploring noncoding RNAs in thyroid cancer using a graph convolutional network approach

期刊

COMPUTERS IN BIOLOGY AND MEDICINE
卷 145, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2022.105447

关键词

Thyroid cancer; microRNA; Long noncoding RNAs; Circular RNA; Graph convolutional network

向作者/读者索取更多资源

This study presents TCGCN, a linear residual graph convolution network, for predicting ncRNA-thyroid cancer associations. By collecting a large amount of ncRNA-disease association data and constructing a bipartite graph, TCGCN combines linear embedding propagation and graph convolutional layers to achieve significantly better performance in prediction.
Noncoding RNAs (ncRNAs) are crucial regulators in initiating and promoting thyroid cancer. Exploring the relationship between ncRNAs and thyroid cancer is essential for the diagnosis and treatment of thyroid cancer. Wet-lab experiments are costly and are difficult to conduct on a large scale. Although there are several ncRNA and cancer-related databases, there are few data related to thyroid cancer. There is a lack of computational approaches for predicting ncRNA-thyroid cancer associations. This work describes TCGCN, a linear residual graph convolution network to predict ncRNA-thyroid cancer associations. We collected a large amount of ncRNA-disease association data and constructed a bipartite graph. We use a simple linear embedding propagation at each convolutional layer and use the weighted sum of the embeddings on all graph convolutional layers to make the final prediction. In 5-fold cross-validation on the ncRNA-thyroid cancer dataset, TCGCN obtained signifi-cantly better performances with an AUC of 0.8162 and an AUPR of 0.8049, which are considerably better than those of other state-of-the-art approaches. We also demonstrate the usability of our method in the case studies.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据