Abstract
There is increasing need for accurate Alzheimer’s disease (AD) related genes prediction to inform study design, but available genes estimates are limited. In this study, the subnetwork extraction algorithms were applied to extract subnetworks and mine candidate genes based on a combined network, which was constructed by integrating the information of protein-protein interactions and gene-gene co-expression network. We obtained seven candidate genes with high possibility during AD progression. The application of subnetwork extraction algorithms based on combined network would provide a new insight into predicting the AD-related genes.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (61672037 and 21601001), the Initial Foundation of Doctoral Scientific Research in Anhui University (J01001319), and Anhui Provincial Outstanding Young Talent Support Plan (No. gxyqZD2017005).
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Wang, X., Yan, H., Zhang, D., Zhao, L., Bin, Y., Xia, J. (2017). Investigating Alzheimer’s Disease Candidate Genes Based on Combined Network Using Subnetwork Extraction Algorithms. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_49
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