An Iterative Method for Predicting Essential Proteins Based on Multifeature Fusion and Linear Neighborhood Similarity - Zhu, Xianyou; Zhu, Yaocan; Tan, Yihong; Chen, Zhiping; Wang, Lei (Frontiers in Aging Neuroscience, 2022)
Growing evidences have demonstrated that that many biological processes are inseparable from the participation of key proteins. In this paper, a novel iterative method called LNSPF is proposed to identify potential key proteins based on multi-feature fusion. In LNSPF, an original PPI network will be constructed first based on known protein-protein interaction data downloaded from benchmark databases, based on which, topological features will be further extracted. Next, gene expression data of proteins will be adopted to transfer the original PPI network to a weighted PPI network based on the linear neighborhood similarity. After that, subcellular localization and homologous information of proteins will be integrated to extract functional features for proteins, and based on both functional and topological features obtained above. And then, an iterative method will be designed and carried out to predict potential key proteins. Finally, for evaluating the predictive performance of LNSPF, extensive experiments have been done, and comparison results between LNPSF and 15 state-of-the-art competitive methods have demonstrated that LNSPF can achieve satisfactory recognition accuracy, which is markedly better than that achieved by each competing method.