A Novel Method for Identifying Essential Proteins Based on Non-negative Matrix Tri-Factorization - Zhang, Zhihong; Jiang, Meiping; Wu, Dongjie; Zhang, Wang; Yan, Wei; Qu, Xilong (Frontiers in Genetics, 2021)
Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent year, there has been an increasing interest in using computational methods to predict essential proteins based on protein-protein interaction (PPI) networks or fusing multiple biological information. However, it has been observed that existing PPI data have false negative and false positive data. The fusion of multiple biological information can reduce the influence of false data in PPI, but inevitably more noise data will be produced at the same time. In this paper, we proposed a novel non-negative matrix tri-factorization (NMTF)-based model (NTMEP) to predict essential proteins. Firstly, a weighted PPI network is established only using the topology features of network, so as to avoid the more noise produced. To reduce the influence of false data (existing in PPI network) on performance of identify essential proteins, NMTF technique, as a widely used recommendation algorithm, is performed to reconstruct a most optimized PPI network which existing more potential protein-protein interactions. Then, we use PageRank algorithm to compute the final ranking score of each protein, in which subcellular localization and homologous information of proteins were used to calculate the initial scores. In addition, extensive experiments are performed on the publicly available datasets and the results indicated that our NTMEP model has better performance of predicting essential proteins against the start-of-the-art method. In this investigation, we demonstrated that the introduction of non-negative matrix tri-factorization technology can effectively improve the condition of protein-protein interaction network, so as to reduce the negative impact of noise on the prediction. At the same time, this finding provides a more novel angle of view for other applications based on protein-protein interaction networks.