Research Area:  Machine Learning

Abstract:

Identification of essential proteins is a hot topic in bioinformatics. In recent years, various traditional methods have been proposed, which usually take topological features to rank the proteins and then set a threshold for selecting essential proteins. Some researchers have also tried to take machine learning methods or deep learning methods for the prediction of essential proteins. However, these methods can not well extract the topological features of protein-protein interaction (PPI) network. Besides, although some scholars proposed to combine biological information with PPI network to reduce the noise of PPI data, how to well combine the biological information with PPI network remains to be a problem. In this paper, we propose a non-local graph neural network to tackle the above problems. In our algorithm, we use graph convolutional network (GCN) layers to extract graph embeddings of PPI network and take three kinds of biological information like gene expression profiles, subcellular localization data, and protein complex data as the features of proteins. Besides, based on the characteristics of our model we design a non-local module as the attention mechanism, which is widely used in convolutional neural networks, to aggregate the information of the graph embeddings. Finally, we use another GCN layer for the node classification and obtain the essential proteins. In the experiments, we evaluated our method based on the widely used S. cerevisiae (Yeast) dataset. The experimental results show that our method can obtain significant improvements over traditional topology-based methods, machine learning-based methods, and recently proposed deep learning-based methods.

Keywords:  
bioinformatics
topological features
proteins
graph convolutional network
deep learning
machine learning

Author(s) Name:  Houwang Zhang, Zhenan Feng, Chong Wu

Publisher name:  IEEE

DOI:  https://doi.org/10.1109/IJCNN55064.2022.9892648

Volume Information:  -

Paper Link: https://ieeexplore.ieee.org/abstract/document/9892648