Abstract
The discovery of miRNAs had great impacts on traditional biology. Typically, miRNAs have the potential to bind to the 3’untraslated region (UTR) of their mRNA target genes for cleavage or translational repression. The experimental identification of their targets has many drawbacks including cost, time and low specificity and these are the reasons why many computational approaches have been developed so far. However, existing computational approaches do not include any advanced feature selection technique and they are facing problems concerning their classification performance and their interpretability. In the present paper, we propose a novel hybrid methodology which combines genetic algorithms and support vector machines in order to locate the optimal feature subset while achieving high classification performance. The proposed methodology was compared with two of the most promising existing methodologies in the problem of predicting human miRNA targets. Our approach outperforms existing methodologies in terms of classification performances while selecting a much smaller feature subset.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bartel, D.P.: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 116(2), 281–297 (2004)
Lagos-Quintana, M., Rauhut, R., Meyer, J., Borkhardt, A., Tuschl, T.: New microRNAs from mouse and human. RNA 9(2), 175–179 (2003)
Lai, E.C.: microRNAs: runts of the genome assert themselves. Curr. Biol. 13(23), R925–R936 (2003)
Mendes, N.D., Freitas, A.T., Sagot, M.-F.: Current tools for the identification of miRNA genes and their targets. Nucleic Acids Res. 37(8), 2419–2433 (2009)
Li, L., Xu, J., Yang, D., Tan, X., Wang, H.: Computational approaches for microRNA studies: a review. Mamm. Genome 21(1-2), 1–12 (2010)
Lewis, B.P., Burge, C.B.: Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120(1), 15–20 (2005)
Grun, D., Wang, Y.L., Langenberger, D., Gunsalus, K.C., Rajewsky, N.: MicroRNA target predictions across seven Drosophila species and comparison to mammalian targets. PLoS Comput. Biol. 1(1), 51–66 (2005)
Enright, A.J., John, B., Gaul, U., Tuschl, T., Sander, C., Marks, D.S.: MicroRNA targets in Drosophila. Genome Biol. 5(1), R1.1–R1.14 (2005)
Kiriakidou, M., Nelson, P.T., Kouranov, A., Fitziev, P., Bouyioukos, C., Mourelatos, Z., Hatzigeorgiou, A.: A combined computational- experimental approach predicts human microRNA targets. Genes Dev. 18, 1165–1178 (2004)
Kim, S.K., Nam, J.W., Rhee, J.K., Lee, W.J., Zhang, B.T.: miTarget: microRNA target-gene prediction using a support vector machine. BMC Bioinformatics 7, 411–422 (2006)
Malik, Y., Jung, S., Kossenkov, A., Showe, L., Showe, M.: Naïve Bayes for microRNA target predictions—machine learning for microRNA targets. Bioinformatics 23(22), 2987–2992 (2007)
Griffiths-Jones, S.: The microRNA Registry. Nucl. Acids Res. 32(suppl. 1), D109–D111 (2004)
Papadopoulos, G.L., Reczko, M., Simossis, V.A., Sethupathy, P., Hatzigeorgiou, A.G.: The database of experimentally supported targets: a functional update of TarBase. Nucleic Acids Res. 37, D155–D158 (2009)
Xiao, F., Zuo, Z., Cai, G., Kang, S., Gao, X., Li, T.: miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 37, D105–D110 (2009)
Saetrom, O., Snøve, O., Saetrom, P.: Weighted sequence motifs as an improved seeding step in microRNA target prediction algorithms. RNA 11, 995–1003 (2005)
Hsu, P.W.: miRNAMAP: genomic maps of microRNA genes and their target genes in mammalian genomes. Nucleic Acids Res. 34, D135–D139 (2006)
Hofacker, I.L.: Vienna RNA secondary structure server. Nucleic Acids Res. 31(13), 3429–3431 (2003)
Lewis, D.P., Jebara, T., Noble, W.S.: Support vector machine learning from heterogeneous data: an empirical analysis using protein sequence and structure. Bioinformatics 22, 2753–2760 (2006)
Holland, J.: Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT Press, Cambridge (1995)
Vapnik, V.N.: The nature of statistical learning theory. Springer (2000)
Jadaan, O., Rao, C.R., Rajamani, L.: Parametric Study to Enhance Genetic Algorithm Performance, Using Ranked based Roulette Wheel Selection method. In: InSciT 2006, Merida, Spain, vol. 2, pp. 274–278 (2006)
Thierens, D.: Adaptive Mutation Rate Control Schemes in Genetic Algorithms. In: Proceedings of the 2002 IEEE World Congress on Computational Intelligence: Congress on Evolutionary Computation, pp. 980–985 (2002)
Mavroudi, S., Katsanos, P., Papadimitriou, S., Likothanassis, S.: Transparent Classification Process of Bioinformatics Data with an Approximated Support Vector Fuzzy Inference System. In: The International Special Topic Conference on Information Technology in Biomedicine (ITAB 2006), Ioannina, Epirus Greece, October 26-28 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aigli, K., Dimitris, K., Konstantinos, T., Spiros, L., Athanasios, T., Seferina, M. (2012). Predicting Human miRNA Target Genes Using a Novel Evolutionary Methodology. In: Maglogiannis, I., Plagianakos, V., Vlahavas, I. (eds) Artificial Intelligence: Theories and Applications. SETN 2012. Lecture Notes in Computer Science(), vol 7297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30448-4_37
Download citation
DOI: https://doi.org/10.1007/978-3-642-30448-4_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-30447-7
Online ISBN: 978-3-642-30448-4
eBook Packages: Computer ScienceComputer Science (R0)