Part of the book series:Lecture Notes in Computer Science ((LNISA,volume 10955))
Included in the following conference series:
2285Accesses
Abstract
Considering the increasingly large scale of gene expression data, common module identification algorithms exist many problems, such as large search space and long running time. A novel co-regulatory modules identification algorithm RMCL-ESA (Regularized Markov Cluster & Explosion Search Algorithm) based on improved Markov cluster and explosion search strategy has been proposed. Improved Markov cluster is adapted to preprocess gene expression profiles through three subprocedure: expansion, inflation, prune, which filter redundant genes and save computational cost. Then, two-stage explosion search strategy has been explored for identifying co-regulatory modules. Comparing with existing methods on breast cancer and ovary cancer datasets from TCGA, CRMs (Co-regulatory Functional Modules) of RMCL-ESA include more significant biological function GO-terms and regulation pathways with high enrichment score.
This is a preview of subscription content,log in via an institution to check access.
Access this chapter
Subscribe and save
- Get 10 units per month
- Download Article/Chapter or eBook
- 1 Unit = 1 Article or 1 Chapter
- Cancel anytime
Buy Now
- Chapter
- JPY 3498
- Price includes VAT (Japan)
- eBook
- JPY 12125
- Price includes VAT (Japan)
- Softcover Book
- JPY 15157
- Price includes VAT (Japan)
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ding, P.J., Luo, J.W., Xiao, Q., Chen, X.T.: A path-based measurement for human miRNA functional similarities using miRNA-disease associations. Sci. Rep.6, 32533 (2016)
Xiao, Q., Luo, J.W., Liang, C., Cai, J., Ding, P.J.: A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations. Bioinformatics34(2), 239–248 (2018)
Luo, J.W., Xiang, G., Pan, C.: Discovery of microRNAs and transcription factors co-regulatory modules by integrating multiple types of genomic data. IEEE Trans. Nanobiosci.16(1), 51–59 (2017)
Shih, Y.K., Parthasarathy, S.: Identifying functional modules in interaction networks through overlapping Markov clustering. Bioinformatics28(18), i473–i479 (2012)
Li, Y., Liang, C., Wong, K.C., et al.: Mirsynergy: detecting synergistic miRNA regulatory modules by overlapping neighbourhood expansion. Bioinformatics30(18), 2627–2635 (2014)
Andrew, Y.N., Michael, I.J., Yair, W.: On spectral clustering: analysis and an algorithm. In: Advance in Neural Information Processing Systems, vol. 2, pp. 849–856 (2002)
Zhang, S.H., Li, Q., Liu, J., Zhou, X.J.: A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA gene regulatory modules. Bioinformatics27(13), 401–409 (2011)
Luo, J., Yin, Y., Chu Pan, G.X., et al.: Identifying functional modules in co-regulatory networks through overlapping spectral clustering. IEEE Trans. Nanobiosci. (2018).https://doi.org/10.1109/TNB.2018.2805846
Tan, Y., Zhu, Y.: Fireworks algorithm for optimization. In: Tan, Y., Shi, Y., Tan, Kay Chen (eds.) ICSI 2010. LNCS, vol. 6145, pp. 355–364. Springer, Heidelberg (2010).https://doi.org/10.1007/978-3-642-13495-1_44
Zhang, Q., Liu, H., Dai, C.: Fireworks explosion optimization algorithm for parameter identification of PV model. In: 2016 IEEE 8th International Power Electronics and Motion Control Conference (IPEMC-ECCE Asia), pp. 1587–1591. IEEE (2016)
Author information
Authors and Affiliations
College of Computer Science and Electronic Engineering of Hunan University, Collaboration and Innovation Center for Digital Chinese Medicine in Hunan Province, Changsha, 410082, Hunan, China
Jiawei Luo & Ying Yin
- Jiawei Luo
You can also search for this author inPubMed Google Scholar
- Ying Yin
You can also search for this author inPubMed Google Scholar
Corresponding authors
Correspondence toJiawei Luo orYing Yin.
Editor information
Editors and Affiliations
Tongji University, Shanghai, China
De-Shuang Huang
University of Ulsan, Ulsan, Korea (Republic of)
Kang-Hyun Jo
Wuhan University of Science and Technology, Wuhan City, China
Xiao-Long Zhang
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Luo, J., Yin, Y. (2018). RMCL-ESA: A Novel Method to Detect Co-regulatory Functional Modules in Cancer. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_93
Download citation
Published:
Publisher Name:Springer, Cham
Print ISBN:978-3-319-95932-0
Online ISBN:978-3-319-95933-7
eBook Packages:Computer ScienceComputer Science (R0)
Share this paper
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative