School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing, China.
Key Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences, Beijing, China.
PLoS One. 2023 Feb 6;18(2):e0281286. doi: 10.1371/journal.pone.0281286. eCollection 2023.
Having observed that gene expressions have a correlation, the Library of Integrated Network-based Cell-Signature program selects 1000 landmark genes to predict the remaining gene expression value. Further works have improved the prediction result by using deep learning models. However, these models ignore the latent structure of genes, limiting the accuracy of the experimental results. We therefore propose a novel neural network named Neighbour Connection Neural Network(NCNN) to utilize the gene interaction graph information. Comparing to the popular GCN model, our model incorperates the graph information in a better manner. We validate our model under two different settings and show that our model promotes prediction accuracy comparing to the other models.
观察到基因表达之间存在相关性后，基于整合网络的细胞特征程序库选择了 1000 个标志性基因来预测其余基因的表达值。进一步的工作通过使用深度学习模型改进了预测结果。然而，这些模型忽略了基因的潜在结构，限制了实验结果的准确性。因此，我们提出了一种名为邻居连接神经网络（NCNN）的新型神经网络，以利用基因交互图信息。与流行的 GCN 模型相比，我们的模型更好地整合了图信息。我们在两种不同的设置下验证了我们的模型，并表明与其他模型相比，我们的模型提高了预测准确性。
