Abstract
microRNAs are single stranded non coding RNA sequences of 18 - 24 nucleotide length. They play an important role in post transcriptional regulation of gene expression. Last decade witnessed identification of hundreds of human microRNAs from genomic data. Experimental as well as computational identification of microRNA binding sites in messenger RNAs are also in progress. Evidences of microRNAs acting as promoter /suppressor of several diseases including cancer are being unveiled. The advancement of Next Generation Sequencing technologies with dramatic reduction in cost, opened endless applications and rapid advances in many fields related to biological science. microRNA expression profiling is a measure of relative abundance of microRNA sequences to the total number of sequences in a sample. Many experiments conducted in this kind of measure proved differential expression of microRNAs in diseased states. This paper discusses an algorithm for microRNA expression profiling, its normalization, and a Support Vector based machine learning approach to develop a Cancer Prediction System. The developed system classify samples with 97.6 % accuracy.
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
Similar content being viewed by others
References
Bartel, D.P.: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116, 281–297 (2004)
Chandra, V.S., Reshmi, G., Achuthsankar, S.N., Sreenathan, S., Radhakrishna, M.P.: Mtar: A computational microrna target prediction architecture for human transcriptome. BMC Bioinform. 10(S1), 1–9 (2010)
Salim, A., Chandra, V.S.: Computational prediction of microRNAs and their targets. J. Proteomics Bioinform. 7(7), 193–202 (2014)
Li, Y., Kowdley, K.V.: MicroRNAs in common human diseases. Genomics Proteomics Bioinform. 10, 246–253 (2012)
Esteller, M.: Non-coding RNAs in human disease. Nat. Rev. Genet. 12, 861–874 (2011)
Ayman, G., Kate, W.: Next-generation sequencing: Methodology and application. Soc. Invest. Dermatol. 133, e11 (2013)
Colin, C.P., Heather, H.C., Muneesh, T.: MicroRNA profiling: approaches and considerations. Nat. Rev. 13, 358–369 (2012)
Shirley, T., de Richard, B., Ming-Sound, T., John, D.M.: Robust global microrna expression profiling using next-generation sequencing technologies. Lab. Invest. 94, 350–358 (2013)
Esquela-Kerscher, A., Frank, J.S.: Oncomirs: microRNAs with a role in cancer. Nat. Rev. 6, 259–269 (2006)
Teresa, M.L., Yingdong, Z., Melissa, R., Jill, K., Hui, L., Andrew, W.B., Maurizia, R., Alisa, M.G., Ilona, L., Francesco, M.M.: MicroRNA expression differentiates histology and predicts survival of lung cancer. Clin. Cancer Res. 16(2), 430–441 (2010)
Boya, X., Ding, Q., Han, H., Wu, D.: MiRCancer: a microRNAcancer association database constructed by text mining on literature. BMC Bioinform. 29, 638–644 (2013)
Andreas, R., Andreas, K., Daniel, S., Felix, B., Barbara, B., Irmtraud, D., Gisela, F., Goar, F., Corinna, M., Fabian, J.T.: Phenomir: A knowledge base for microrna expression in diseases and biological processes. Genome Biology
Kozomara, A., Griffiths-Jones, S.: MiRBase: Annotating high confidence micrornas using deep sequencing data. Nucleic Acids Res. 42, D68–D73 (2014)
Seda, E., Danos, C.C., Francois, V., George, M.C., Seidman, J.G.: Quantification of microRNA expression with next-generation sequencing. Curr. Protoc. Mol. Biol. 4(16), 1–20 (2013)
Nuno, A.F., Johan, R., Alvis, B., John C.M.: Tools for mapping high-throughput sequencing data. Bioinform. Adv. Access (2012)
Kristina, S., Susanne, L., Molton, M.W., Clara-Cecilie, G., Marit, H., Eivind, H., Oystein, F., Leonardo, A.M.Z., Kjersti, F.: Deep sequencing the microRNA transcriptome in colorectal cancer. PLOS-ONE 8(6), 3169–3177 (2013)
Johannes, H.S., Tobias, M., Marcel, M., Philipp, R., Pieter, M., Stefanie, S., Theresa, T., Jo, V., Angelika, E., Stefan, S., Sven, R., Alexander, S.: Deep sequencing reveals differential expression of micrornas in favorable versus unfavorable neuroblastoma. Nucleic Acids Res. 38(17), 5919–5928 (2010)
Hong-Tai, C., Sung-Chou, L., Meng-Ru, H., Hung-Wei, P., Luo-Ping, G., Ling-Yueh, H., Shou-Yu, Y., Wen-Hsiung, L., Kuo-Wang, T.: Comprehensive analysis of microRNAs in breast cancer. BMC Genomics 13(6), s18 (2012)
Horspool, R.N.: Practical fast searching in strings. Softw. Pract. Experience 10, 501506 (1980)
Meyer, S.U., Pfaffl, M.W., Ulbrich, S.E.: Normalization strategies for microrna profiling experiments: a normal way to a hidden layer of complexity? Biotechnol. Lett. 10(1007), 1777–1788 (2010)
Anil, J., Karthik, N., Arun, R.: Score normalization in multimodal biometric systems. Pattern Recogn. 38, 2270–2285 (2005)
Isabelle, G., Andre, E.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
A., S., R., A., S. S., V.C. (2016). SVM Based Lung Cancer Prediction Using microRNA Expression Profiling from NGS Data. In: Nguyen, N.T., Trawiński, B., Fujita, H., Hong, TP. (eds) Intelligent Information and Database Systems. ACIIDS 2016. Lecture Notes in Computer Science(), vol 9621. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49381-6_52
Download citation
DOI: https://doi.org/10.1007/978-3-662-49381-6_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-49380-9
Online ISBN: 978-3-662-49381-6
eBook Packages: Computer ScienceComputer Science (R0)