Abstract
Identifying cancer drivers is critical to advancing cancer research and personalized medicine. Most methods for identifying cancer drivers focus on the entire genes or a single mutation site. But not all mutations in a gene have the same effect, the consequences of which usually depend on the position in the protein and amino acid change. The intermediate level of analysis between individual locations and the entire gene may give us better statistics and better resolution than the former. Here, we developed prDriver, a Bayesian hierarchical modeling method that identifies regions of proteins with high functional impact scores and significant effects on gene expression levels. Our study highlights the importance of integrating multi-omics data in predicting cancer driver and provides a statistically rigorous solution for cancer target discovery and development.
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Acknowledgments
This work was supported by National Natural Science Foundation of China (Grant No. 61502159) and Natural Science Foundation of Hunan Province, China (Grant No. 2018JJ2053).
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Lu, X., Wang, X., Liu, P., Zhu, Z., Ding, L. (2019). A Novel Method to Predict Protein Regions Driving Cancer Through Integration of Multi-omics Data. In: Huang, DS., Jo, KH., Huang, ZK. (eds) Intelligent Computing Theories and Application. ICIC 2019. Lecture Notes in Computer Science(), vol 11644. Springer, Cham. https://doi.org/10.1007/978-3-030-26969-2_29
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DOI: https://doi.org/10.1007/978-3-030-26969-2_29
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