In biology, text-mining is widely used to extract relationships between biological entities. Gene prioritization is also important to analyze diseases, because mutated or dysregulated genes play an important role in pathogenesis. Here, we propose a method to identify disease-related genes using seed genes and network analysis. We constructed an integrating gene network for lung cancer by combining local gene networks for seed genes. Analyzing the integrating gene network, we inferred meaningful lung cancer-related genes and potential candidate genes. We also demonstrated that our method is more useful for extracting disease-gene relationships than previous methods. In this study, we extracted 21 lung cancer related genes and 11 candidate genes with supporting evidence of their association with lung cancer.
|Title of host publication||2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||6|
|Publication status||Published - 2017 Nov 27|
|Event||2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017 - Banff, Canada|
Duration: 2017 Oct 5 → 2017 Oct 8
|Name||2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017|
|Other||2017 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2017|
|Period||17/10/5 → 17/10/8|
Bibliographical noteFunding Information:
Sanghyun Park is the corresponding author. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (NRF-2017M2A2A7A02020213).
© 2017 IEEE.
All Science Journal Classification (ASJC) codes
- Artificial Intelligence
- Computer Science Applications
- Human-Computer Interaction
- Control and Optimization