عنوان انگلیسی مقاله:
Glioma stages prediction based on machine learning algorithm combined with protein-protein interaction networks
ترجمه فارسی عنوان مقاله:
پیش بینی مراحل گلیوما بر اساس الگوریتم یادگیری ماشین همراه با شبکه های تعامل پروتئین-پروتئین
Sciencedirect - Elsevier - Genomics,Corrected proof,doi:10:1016/j:ygeno:2019:05:024
Bing Niua,f,⁎,1, Chaofeng Liangb,1, Yi Lua,1, Manman Zhaoa, Qin Chena, Yuhui Zhangc,g,⁎, Linfeng Zhengd,h,⁎, Kuo-Chen Choue,f
Background: Glioma is the most lethal nervous system cancer. Recent studies have made great efforts to study
the occurrence and development of glioma, but the molecular mechanisms are still unclear. This study was
designed to reveal the molecular mechanisms of glioma based on protein-protein interaction network combined
with machine learning methods. Key differentially expressed genes (DEGs) were screened and selected by using
the protein-protein interaction (PPI) networks.
Results: As a result, 19 genes between grade I and grade II, 21 genes between grade II and grade III, and 20 genes
between grade III and grade IV. Then, five machine learning methods were employed to predict the gliomas
stages based on the selected key genes. After comparison, Complement Naive Bayes classifier was employed to
build the prediction model for grade II-III with accuracy 72.8%. And Random forest was employed to build the
prediction model for grade I-II and grade III-VI with accuracy 97.1% and 83.2%, respectively. Finally, the selected
genes were analyzed by PPI networks, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and
Genomes (KEGG) pathways, and the results improve our understanding of the biological functions of select DEGs
involved in glioma growth. We expect that the key genes expressed have a guiding significance for the occurrence
of gliomas or, at the very least, that they are useful for tumor researchers.
Conclusion: Machine learning combined with PPI networks, GO and KEGG analyses of selected DEGs improve
our understanding of the biological functions involved in glioma growth.
Keywords: DEGs | Machine learning | PPI networks | GO | KEGG | SVM | ANN | Random forest | Couple naïve Bayes