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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References
Lee, W.-P., Tzou, W.-S.: Computational methods for discovering gene networks from expression data. Briefings Bioinform. 10, 408–423 (2009)
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)
Ma, S., Dai, Y.: Principal component analysis based methods in bioinformatics studies. Briefings Bioinform. 12, 714–722 (2011)
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)
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). 292063
Saadatpoura, A., Albert, R.: Boolean modeling of biological regulatory networks: a methodology tutorial. Methods 62, 3–12 (2013)
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)
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)
Bezdek, J.: FCM: the fuzzy c-means clustering algorithm. Comput. Geosci. 10, 191–203 (1981)
Kustra, R., Zagdanski, A.: Data-fusion in clustering microarray data balancing discovery and interpretability. IEEE/ACM Trans. Comput. Biol. Bioinform. 7, 50–63 (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)
Mussel, C., Hopfensitz, M., Kestler, H.A.: Boolnet—an R package for generation, reconstruction and analysis of boolean networks. Bioinformatics 26, 1378–1380 (2012)
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Lin, CH., Hsiao, YT., Lee, WP. (2015). Inferring Large Gene Networks with a Hybrid Fuzzy Clustering Method. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_71
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DOI: https://doi.org/10.1007/978-3-319-22180-9_71
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