Abstract
Many computational algorithms were introduced to interpret the gene expressions data, and most of them were not robust enough to scale and classify large-scale gene population. Hence, a novel modified Manhattan distance-based weighted gene expression graph (GEG) classifier is proposed. Here, the gene data points were considered as one of the prominent features and extracted using the proposed modified Manhattan distance. Here a new classification scheme is attempted by combining modified Manhattan distance based on weighted GEG gene graph classifier. Further, the proposed model is experimentally validated and its performance is compared with conventional classifiers like naive Bayes, SVM and random forest, etc. The results show that the proposed model is robust and achieves better accuracy in classification.
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
Benso, A., Di Carlo, S., Politano, G., Sterpone, L.: A Graph-based representation of gene expression profiles in DNA microarrays. In: Proceedings of IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 75–82 (2008)
Breiman, J., ad Friedman, L., Stone, C.J., Olshen, R.: Classification and Regression Trees. Taylor and Francis, New York (1984)
Zhang, H., Yu, C.-Y., Singer, B.: Cell and tumor classification using gene expression data: construction of forests. Proc. Nat. Acad. Sci. USA 100(7), 4168–4172 (2003)
Vapnik, V.N.: An overview of statistical learning theory. IEEE Trans. Neural Netw. 10(5), 988–999 (1999)
Khan, J., Wei, J.S., Ringner, M., Saal, L.H., Ladanyi, M., Wester-mann, F., Berthold, F., Schwab, M., Antonescu, C.R., Peterson, C., Meltzer, P.S.: Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat. Med. 7(6), 673–679 (2001)
Benso, A., Di Carlo, S., Politano, G.: A cDNA microarray gene expression data classifier for clinical diagnostics based on graph theory. IEEE/ACM Trans. Comput. Biol. Bioinform. 8(3), 577–591 (2011)
Zheng, H., Ng, T.Y., Zhang, L., Shiu, C.K., Wang, H.Q.: Tumor classification based on non-negative matrix factorization using gene expression data. IEEE Trans. Nanobiosci. 10(2), 86–93 (2011)
Chiang, J.H., Ho, S.H.: A combination of rough-based feature selection and RBF neural network for classification using gene expression data. IEEE Trans. Nanobiosci. 7(1), 91–99 (2008)
Das, R., Saha, S.: Gene expression classification using a fuzzy point symmetry based PSO clustering technique. In: 2015 Second International Conference on Soft Computing and Machine Intelligence (ISCMI), Hong Kong, pp. 69–73 (2015)
Bontempi, G.: A blocking strategy to improve gene selection for classification of gene expression data. IEEE/ACM Trans. Comput. Biol. Bioinform. 4(2), 293–300 (2007)
Young, M., Craft, D.: Pathway-informed classification system (PICS) for cancer analysis using gene expression data. Cancer Inform. 15, 151–161 (2016)
Ismail, A.G., Ablahad, A.A.: Novel method for mutational disease prediction using bioinformatics techniques and backpropagation algorithm. IRACST – Eng. Sci. Technol. Int. J. (ESTIJ) (2013). ISSN: 2250-3498
Xu, J., Li, Y.: Discovering disease-genes by topological features in human protein–protein interaction network. Bioinformatics (2006)
Klasberg, S., Bitard-Feildel, T., Mallet, L.: Computational identification of novel genes: current and future perspectives, current and future perspectives. Bioinform. Biol. Insights 10, 121–131 (2016)
Yan, W., Xue, W., Chen, J., Hu, G.: Biological networks for cancer candidate biomarkers discovery. Cancer Inform. 15(S3), 1–7 (2016)
Ganesan, K., Lloyd, S., Sarkar, V.: Discovering related clinical concepts using large amounts of clinical notes. Biomed. Eng. Comput. Biol. 7(S2), 27–33 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sevugapandi, N., Chandran, C.P. (2019). A Robust Gene Data Classification Model Using Modified Manhattan Distance-Based Weighted Gene Expression Graph Classifier. In: Satapathy, S., Bhateja, V., Das, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 104. Springer, Singapore. https://doi.org/10.1007/978-981-13-1921-1_50
Download citation
DOI: https://doi.org/10.1007/978-981-13-1921-1_50
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1920-4
Online ISBN: 978-981-13-1921-1
eBook Packages: EngineeringEngineering (R0)