Identification of essential proteins based on edge features and the fusion of multiple-source biological information

Abstract

Background A major current focus in the analysis of protein–protein interaction (PPI) data is how to identify essential proteins. As massive PPI data are available, this warrants the design of efficient computing methods for identifying essential proteins. Previous studies have achieved considerable performance. However, as a consequence of the features of high noise and structural complexity in PPIs, it is still a challenge to further upgrade the performance of the identification methods. Methods This paper proposes an identification method, named CTF, which identifies essential proteins based on edge features including h-quasi-cliques and uv-triangle graphs and the fusion of multiple-source information. We first design an edge-weight function, named EWCT, for computing the topological scores of proteins based on quasi-cliques and triangle graphs. Then, we generate an edge-weighted PPI network using EWCT and dynamic PPI data. Finally, we compute the essentiality of proteins by the fusion of topological scores and three scores of biological information. Results We evaluated the performance of the CTF method by comparison with 16 other methods, such as MON, PeC, TEGS, and LBCC, the experiment results on three datasets of Saccharomyces cerevisiae show that CTF outperforms the state-of-the-art methods. Moreover, our method indicates that the fusion of other biological information is beneficial to improve the accuracy of identification.

ECDEP: Identifying essential proteins based on evolutionary community discovery and subcellular localization

“…JDC [23], for instance, utilizes threshold calculations to binarize network fluctuations, subsequently combining degree centrality and the Jaccard similarity index to compute JDC scores. Meanwhile, CTF [24] identifies essential proteins through edge features and multi-source information fusion, culminating in edge-weighted PPI networks entwined with dynamic PPI data. The combination of gene expression data and its utilization to provide dynamism constitutes a widely adopted approach among researchers.…”