A novel method to predict essential proteins based on tensor and HITS algorithm

• Published in: Human genomics Vol. 14; no. 1; p. 14
• Main Authors: Zhang, Zhihong, Luo, Yingchun, Hu, Sai, Li, Xueyong, Wang, Lei, Zhao, Bihai
• Format: Journal Article
• Language: English
• Published: England BioMed Central 06.04.2020; BMC
• Subjects: Algorithms; Annotations; Computational Biology - methods; Computer applications; Computer Simulation; Gene expression; Methods; Predictions; Primary Research; Protein interaction; Protein Interaction Maps; Proteins; Research methodology; Saccharomyces cerevisiae - metabolism; Saccharomyces cerevisiae Proteins - metabolism
• DOI: 10.1186/s40246-020-00263-7
• ISSN: 1479-7364; 1473-9542

Essential proteins are an important part of the cell and closely related to the life activities of the cell. Hitherto, Protein-Protein Interaction (PPI) networks have been adopted by many computational methods to predict essential proteins. Most of the current approaches focus mainly on the topological structure of PPI networks. However, those methods relying solely on the PPI network have low detection accuracy for essential proteins. Therefore, it is necessary to integrate the PPI network with other biological information to identify essential proteins. In this paper, we proposed a novel random walk method for identifying essential proteins, called HEPT. A three-dimensional tensor is constructed first by combining the PPI network of Saccharomyces cerevisiae with multiple biological data such as gene ontology annotations and protein domains. Then, based on the newly constructed tensor, we extended the Hyperlink-Induced Topic Search (HITS) algorithm from a two-dimensional to a three-dimensional tensor model that can be utilized to infer essential proteins. Different from existing state-of-the-art methods, the importance of proteins and the types of interactions will both contribute to the essential protein prediction. To evaluate the performance of our newly proposed HEPT method, proteins are ranked in the descending order based on their ranking scores computed by our method and other competitive methods. After that, a certain number of the ranked proteins are selected as candidates for essential proteins. According to the list of known essential proteins, the number of true essential proteins is used to judge the performance of each method. Experimental results show that our method can achieve better prediction performance in comparison with other nine state-of-the-art methods in identifying essential proteins. Through analysis and experimental results, it is obvious that HEPT can be used to effectively improve the prediction accuracy of essential proteins by the use of HITS algorithm and the combination of network topology with gene ontology annotations and protein domains, which provides a new insight into multi-data source fusion.