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DPPN-SVM: Computational Identification of Mis-Localized Proteins in Cancers by Integrating Differential Gene Expressions With Dynamic Protein-Protein Interaction Networks.
- Source :
-
Frontiers in genetics [Front Genet] 2020 Oct 23; Vol. 11, pp. 600454. Date of Electronic Publication: 2020 Oct 23 (Print Publication: 2020). - Publication Year :
- 2020
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Abstract
- Eukaryotic cells contain numerous components, which are known as subcellular compartments or subcellular organelles. Proteins must be sorted to proper subcellular compartments to carry out their molecular functions. Mis-localized proteins are related to various cancers. Identifying mis-localized proteins is important in understanding the pathology of cancers and in developing therapies. However, experimental methods, which are used to determine protein subcellular locations, are always costly and time-consuming. We tried to identify cancer-related mis-localized proteins in three different cancers using computational approaches. By integrating gene expression profiles and dynamic protein-protein interaction networks, we established DPPN-SVM (Dynamic Protein-Protein Network with Support Vector Machine), a predictive model using the SVM classifier with diffusion kernels. With this predictive model, we identified a number of mis-localized proteins. Since we introduced the dynamic protein-protein network, which has never been considered in existing works, our model is capable of identifying more mis-localized proteins than existing studies. As far as we know, this is the first study to incorporate dynamic protein-protein interaction network in identifying mis-localized proteins in cancers.<br /> (Copyright © 2020 Li, Du, Shen, Liu and Luo.)
Details
- Language :
- English
- ISSN :
- 1664-8021
- Volume :
- 11
- Database :
- MEDLINE
- Journal :
- Frontiers in genetics
- Publication Type :
- Academic Journal
- Accession number :
- 33193746
- Full Text :
- https://doi.org/10.3389/fgene.2020.600454