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2015 | OriginalPaper | Buchkapitel

Inferring Large Gene Networks with a Hybrid Fuzzy Clustering Method

verfasst von : Chung-Hsun Lin, Yu-Ting Hsiao, Wei-Po Lee

Erschienen in: Intelligent Computing Theories and Methodologies

Verlag: Springer International Publishing

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Abstract

To tackle the scalability problem in reverse engineering gene networks, this study presents an approach with two phases: gene clustering and network reconstruction. For gene clustering, a hybrid data and knowledge-driven method is developed to calculate similarity between genes. In the network reconstruction procedure, a Boolean network model is inferred from gene clusters. A series of experiments are conducted to investigate the effect of the hybrid similarity measure in gene clustering and network reconstruction. The results prove the feasibility and effectiveness of the proposed approach.

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Literatur
1.
Zurück zum Zitat Lee, W.-P., Tzou, W.-S.: Computational methods for discovering gene networks from expression data. Briefings Bioinform. 10, 408–423 (2009) Lee, W.-P., Tzou, W.-S.: Computational methods for discovering gene networks from expression data. Briefings Bioinform. 10, 408–423 (2009)
2.
Zurück zum Zitat Chai, L.E., Loh, S.K., Low, S.T., et al.: A review on the computational approaches for gene regulatory network construction. Comput. Biol. Med. 48, 55–65 (2014)CrossRefMATH Chai, L.E., Loh, S.K., Low, S.T., et al.: A review on the computational approaches for gene regulatory network construction. Comput. Biol. Med. 48, 55–65 (2014)CrossRefMATH
3.
Zurück zum Zitat Ma, S., Dai, Y.: Principal component analysis based methods in bioinformatics studies. Briefings Bioinform. 12, 714–722 (2011)MathSciNetCrossRef Ma, S., Dai, Y.: Principal component analysis based methods in bioinformatics studies. Briefings Bioinform. 12, 714–722 (2011)MathSciNetCrossRef
4.
Zurück zum Zitat Tan, M., Alshalalfa, M., Alhajj, R., Polat, F.: Influence of prior knowledge in constraint-based learning of gene regulatory networks. IEEE Trans. Comput. Biol. Bioinform. 8, 130–142 (2011)CrossRef Tan, M., Alshalalfa, M., Alhajj, R., Polat, F.: Influence of prior knowledge in constraint-based learning of gene regulatory networks. IEEE Trans. Comput. Biol. Bioinform. 8, 130–142 (2011)CrossRef
5.
Zurück zum Zitat Mazandu, G.K., Mulder, N.J.: Information content-based gene ontology semantic similarity approaches: toward a unified framework theory. Biomed Res. Int. 2013, 1–5 (2013). 292063CrossRef Mazandu, G.K., Mulder, N.J.: Information content-based gene ontology semantic similarity approaches: toward a unified framework theory. Biomed Res. Int. 2013, 1–5 (2013). 292063CrossRef
6.
Zurück zum Zitat Saadatpoura, A., Albert, R.: Boolean modeling of biological regulatory networks: a methodology tutorial. Methods 62, 3–12 (2013)CrossRef Saadatpoura, A., Albert, R.: Boolean modeling of biological regulatory networks: a methodology tutorial. Methods 62, 3–12 (2013)CrossRef
7.
Zurück zum Zitat Resnik, P.: Semantic similarity in a taxonomy: an information based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. 11, 95–130 (1999)MATH Resnik, P.: Semantic similarity in a taxonomy: an information based measure and its application to problems of ambiguity in natural language. J. Artif. Intell. Res. 11, 95–130 (1999)MATH
8.
Zurück zum Zitat Wang, J.Z., Du, Z., Payattakool, R., Yu, P.S., Chen, C.-F.: A new method to measure the semantic similarity of go terms. Bioinformatics 23, 1274–1281 (2007)CrossRef Wang, J.Z., Du, Z., Payattakool, R., Yu, P.S., Chen, C.-F.: A new method to measure the semantic similarity of go terms. Bioinformatics 23, 1274–1281 (2007)CrossRef
9.
Zurück zum Zitat Bezdek, J.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10, 191–203 (1981)CrossRef Bezdek, J.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10, 191–203 (1981)CrossRef
10.
Zurück zum Zitat Kustra, R., Zagdanski, A.: Data-fusion in clustering microarray data balancing discovery and interpretability. IEEE/ACM Trans. Comput. Biol. Bioinform. 7, 50–63 (2010)CrossRef Kustra, R., Zagdanski, A.: Data-fusion in clustering microarray data balancing discovery and interpretability. IEEE/ACM Trans. Comput. Biol. Bioinform. 7, 50–63 (2010)CrossRef
11.
Zurück zum Zitat Zainudin, S., Mohamed, N.S.: Evaluating the performance of partitioning techniques for gene network inference. In: Proceedings of International Conference on Intelligent Systems Design and Applications, pp. 1119–1124 (2010) Zainudin, S., Mohamed, N.S.: Evaluating the performance of partitioning techniques for gene network inference. In: Proceedings of International Conference on Intelligent Systems Design and Applications, pp. 1119–1124 (2010)
12.
Zurück zum Zitat Mussel, C., Hopfensitz, M., Kestler, H.A.: Boolnet—an R package for generation, reconstruction and analysis of boolean networks. Bioinformatics 26, 1378–1380 (2012)CrossRef Mussel, C., Hopfensitz, M., Kestler, H.A.: Boolnet—an R package for generation, reconstruction and analysis of boolean networks. Bioinformatics 26, 1378–1380 (2012)CrossRef
Metadaten
Titel
Inferring Large Gene Networks with a Hybrid Fuzzy Clustering Method
verfasst von
Chung-Hsun Lin
Yu-Ting Hsiao
Wei-Po Lee
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-22180-9_71