Abstract
Identifying essential proteins is important for understanding cellular life and pathogenic mechanisms. In addition to experimental methods, many computing approaches have been employed to identify essential proteins. Many of these methods usually consider the topological structure of the protein-protein interaction network (PPIN). However, the fact that the PPIN includes many false negative interactions is ignored. In this paper, an integrated PPIN that considers protein interactions and gene co-expression is presented. With more false negative interactions being considered, more meaningful essential proteins can be identified on the integrated PPIN. To verify the performance of this method, an experiment is carried out on data from Saccharomyces cerevisiae. The results demonstrate that ITPPIN can identify essential proteins is more effective than PPIN.
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This work was supported in part by the NSFC under grant Nos. 61872220, and 61873001.
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Wen, CG., Liu, JX., Qin, L., Wang, J., Fang, Y. (2020). Essential Proteins Identification Based on Integrated Network. In: Huang, DS., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2020. Lecture Notes in Computer Science(), vol 12463. Springer, Cham. https://doi.org/10.1007/978-3-030-60799-9_7
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