Combinational logic network for digitally coded gene expression of gastric cancer
- Authors
- Park, S.; Nam, S.
- Issue Date
- 2017
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Boolean models; Cancer; Logical networks; Network models
- Citation
- Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, pp.1777 - 1782
- Journal Title
- Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
- Start Page
- 1777
- End Page
- 1782
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/6635
- DOI
- 10.1109/BIBM.2016.7822788
- ISSN
- 0000-0000
- Abstract
- In general, Boolean networks have been addressed in time-series datasets. However, in the recent field of next-generation sequencing-based cancer genomics, cross-sectional data sets having enormous numbers of patients have been accumulated. Here, we deal with representation of cross-sectional datasets using Boolean networks, and specifically, combinational logic network approach. We then applied the approach to a real cancer patient dataset, demonstrating the feasibility of using Boolean networks in graphical representation of cross-sectional datasets. © 2016 IEEE.
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