Abstract
Along with the rapid development of high-throughput sequencing technology, a large amount of multi-omics data sets are generated, which provide more opportunities to understand the mechanism of complex diseases. In this study, an improved particle swarm optimization with dynamic scale-free network, named DSFPSO, is proposed for detecting multi-omics features. The highlights of DSFPSO are the introduced scale-free network and velocity updating strategies. The scale-free network is employed to DSFPSO as its population structure, which can dynamically adjust the iteration processes. Three types of velocity updating strategies are used in DSFPSO for fully considering the heterogeneity of particles and their neighbors. Both gene function analysis and pathway analysis on colorectal cancer (CRC) data show that DSFPSO can detect CRC-associated features effectively.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bersanelli, M., Mosca, E., Remondini, D., et al.: Methods for the integration of multi-omics data: mathematical aspects. BMC Bioinformatics 17(2), S15 (2016)
Roth, V.: The generalized LASSO: a wrapper approach to gene selection for microarray data. Sekretariat für Forschungsberichte, Inst. für Informatik III (2002)
Liu, J.X., Gao, Y.L., Xu, Y., et al.: Differential expression analysis on RNA-Seq count data based on penalized matrix decomposition. IEEE Trans. Nanobiosci. 13(1), 12–18 (2014)
Luss, R., d’Aspremont, A.: Clustering and feature selection using sparse principal component analysis. Optim. Eng. 11(1), 145–157 (2010)
Zhang, W., Shang, J., Li, H., Sun, Y., Liu, J.X.: SIPSO: Selectively Informed Particle Swarm Optimization Based on Mutual Information to Determine SNP-SNP Interactions. In: Huang, D.-S., Bevilacqua, V., Premaratne, P. (eds.) ICIC 2016. LNCS, vol. 9771, pp. 112–121. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-42291-6_11
Chuang, L.Y., Chang, H.W., Tu, C.J., et al.: Improved binary PSO for feature selection using gene expression data. Comput. Biol. Chem. 32(1), 29–38 (2008)
Kar, S., Sharma, K.D., Maitra, M.: Gene selection from microarray gene expression data for classification of cancer subgroups employing PSO and adaptive K-nearest neighborhood technique. Expert Syst. Appl. 42(1), 612–627 (2015)
He, R.: An improved particle swarm optimization based on self-adaptive escape velocity. J. Softw. 16(12), 2036–2044 (2005)
Kennedy, J., Mendes, R.: Population structure and particle swarm performance. In: Proceedings of the 2002 Congress on evolutionary computation 2002 CEC 2002, vol. 2, pp. 1671–1676. IEEE (2002)
Liu, C., Du, W.B., Wang, W.X.: Particle swarm optimization with scale-free interactions. PLoS ONE 9(5), e97822 (2014)
Gao, Y., Du, W., Yan, G.: Selectively-informed particle swarm optimization. Sci. Rep. 5, 9295 (2015)
Zhao, S.Z., Liang, J.J., Suganthan, P.N., et al.: Dynamic multi-swarm particle swarm optimizer with local search for large scale global optimization. In: IEEE Congress on 2008 Evolutionary Computation (IEEE World Congress on Computational Intelligence), pp. 3845–3852. IEEE (2008)
Shi, Y.: Particle swarm optimization: developments, applications and resources evolutionary computation. In: Proceedings of the 2001 Congress on 2001, vol. 1, pp. 81–86. IEEE (2001)
Shang, J., Sun, Y., Li, S., et al.: An improved opposition-based learning particle swarm optimization for the detection of SNP-SNP interactions. Biomed Res. Int. 2015, 524821 (2015)
Liu, J.X., Gao, Y.L., Zheng, C.H., et al.: Block-constraint robust principal component analysis and its application to integrated analysis of TCGA data. IEEE Trans. Nanobiosci. 15(6), 510–516 (2016)
Lee, H.J., Flaherty, P., Ji, H.P.: Systematic genomic identification of colorectal cancer genes delineating advanced from early clinical stage and metastasis. BMC Med. Genomics 6(1), 1 (2013)
Kanehisa, M., Araki, M., Goto, S., et al.: KEGG for linking genomes to life and the environment. Nucleic Acids Res. 36(suppl_1), D480–D484 (2007)
Li, N., Bu, X., Tian, X., et al.: Fatty acid synthase regulates proliferation and migration of colorectal cancer cells via HER2-PI3 K/Akt signaling pathway. Nutr. Cancer 64(6), 864–870 (2012)
Josse, C., Bouznad, N., Geurts, P., et al.: Identification of a microRNA landscape targeting the PI3 K/Akt signaling pathway in inflammation-induced colorectal carcinogenesis. Am. J. Physiol. Gastrointest. Liver Physiol. 306(3), G229–G243 (2014)
Zhang, Y., Gan, B., Liu, D., et al.: FoxO family members in cancer. Cancer Biol. Ther. 12(4), 253–259 (2011)
De Mattos, S.F., Villalonga, P., Clardy, J., et al.: FOXO3a mediates the cytotoxic effects of cisplatin in colon cancer cells. Mol. Cancer Ther. 7(10), 3237–3246 (2008)
Mees, S.T., Mennigen, R., Spieker, T., et al.: Expression of tight and adherens junction proteins in ulcerative colitis associated colorectal carcinoma: upregulation of claudin-1, claudin-3, claudin-4, and β-catenin. Int. J. Colorectal Dis. 24(4), 361–368 (2009)
Webb, E.L., Rudd, M.F., Sellick, G.S., et al.: Search for low penetrance alleles for colorectal cancer through a scan of 1467 non-synonymous SNPs in 2575 cases and 2707 controls with validation by kin-cohort analysis of 14 704 first-degree relatives. Hum. Mol. Genet. 15(21), 3263–3271 (2006)
Zhang, R., Song, C.: Loss of CSMD1 or 2 may contribute to the poor prognosis of colorectal cancer patients. Tumor Biol. 35(5), 4419–4423 (2014)
Gong, J., Tian, J., Lou, J., et al.: A polymorphic MYC response element in KBTBD11 influences colorectal cancer risk, especially in interaction with a MYC regulated SNP rs6983267. Ann. Oncol. 29(3), 632–639 (2018)
Kawasaki, T., Ohnishi, M., Suemoto, Y., et al.: WRN promoter methylation possibly connects mucinous differentiation, microsatellite instability and CpG island methylator phenotype in colorectal cancer. Mod. Pathol. 21(2), 150 (2008)
Liu, Y.L., Gao, X., Jiang, Y., et al.: Expression and clinicopathological significance of EED, SUZ12 and EZH2 mRNA in colorectal cancer. J. Cancer Res. Clin. Oncol. 141(4), 661–669 (2015)
Da Costa, L.T., He, T.C., Yu, J., et al.: CDX2 is mutated in a colorectal cancer with normal APC/β-catenin signaling. Oncogene 18(35), 5010 (1999)
Brabletz, T., Spaderna, S., Kolb, J., et al.: Down-regulation of the homeodomain factor Cdx2 in colorectal cancer by collagen type I: an active role for the tumor environment in malignant tumor progression. Cancer Res. 64(19), 6973–6977 (2004)
Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61502272, 61572284); Project of Shandong Province Higher Educational Science and Technology Program (J18KA373); the Scientific Research Foundation of Qufu Normal University (BSQD20130119); the Science and Technology Planning Project of Qufu Normal University (xkj201524).
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that there is no conflict of interests regarding the publication of this paper.
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Li, H., Li, SJ., Shang, J., Liu, JX., Zheng, CH. (2018). An Improved Particle Swarm Optimization with Dynamic Scale-Free Network for Detecting Multi-omics Features. In: Zhang, F., Cai, Z., Skums, P., Zhang, S. (eds) Bioinformatics Research and Applications. ISBRA 2018. Lecture Notes in Computer Science(), vol 10847. Springer, Cham. https://doi.org/10.1007/978-3-319-94968-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-319-94968-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94967-3
Online ISBN: 978-3-319-94968-0
eBook Packages: Computer ScienceComputer Science (R0)