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Khan, Mohd. Faheem
- An Approach to overcome Imbalance Datasets of Eukaryotic Genomes during the Analysis by Machine Learning Technique (SVM)
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Authors
Affiliations
1 Department of Plant Sciences, MJP Rohilkhand University Bareilly (U.P), IN
2 Bioinformatics Centre, IVRI, Izatnagar, Bareilly-243122 (U.P), IN
1 Department of Plant Sciences, MJP Rohilkhand University Bareilly (U.P), IN
2 Bioinformatics Centre, IVRI, Izatnagar, Bareilly-243122 (U.P), IN
Source
Indian Journal of Science and Technology, Vol 4, No 5 (2011), Pagination: 520-524Abstract
In biology, Support Vector Machines (SVM) is most frequently used tool for the analysis of gene expression, microarray experiments and other biological applications. In human genome dataset, only a small proportion of the DNA sequences represent genes, and the rest do not. In our work, we highlighted the reasons why, particular SVM, fails and what can be done to overcome this.Keywords
Imbalanced Dataset, BioSVMFull Text
References
- Akbani R, Kwek S and Japkowicz N (2004) Applying support vector machines to imbalanced datasets. Proc. 15th Eur. Conf. on Machine Learning (ECML). Pisa, Italy, Sept., Springer-Verlag, Germany. pp: 39- 50.
- Chawla N, Bowyer K, Hall L and Kegelmeyer W (2002) SMOTE: Synthetic Minority Over-sampling Technique. J. Artificial Intelligence Res. 16, 321-357.
- Cristianini N and Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, Cambridge, UK. ISBN 0521780195.
- Joachims T (1998) Text categorization with SVM: Learning with many relevant features. Proc. 10th Eur. Conf. on Machine Learning (ECML).
- Kozak M (1996) Interpreting cDNA sequences: Some insights from studies on translation. Mammalian Genome. 7, 563-574.
- Vapnik V (1995) The nature of statistical learning theory. Springer, NY. ISBN 0387987800.
- Veropoulos K, Campbell C and Cristianini N (1999) Controlling the sensitivity of support vector machines. Proc. Intl. Joint Conf. on AI. pp: 55–60.
- Wu G and Chang E (2003) Class-Boundary alignment for imbalanced dataset learning. Proc. ICML 2003 Workshop on Learning from Imbalanced Data Sets II, Washington DC, USA.
- Zeng F, Yap HC and Wong L (2002) Using feature generation and feature selection for accurate prediction of translation initiation sites. Proc. of 13th Workshop on Genome Informatics, Universal Academy Press. pp: 192-200.
- Target Binding Hotspot Secrets of mir1 microRNA in Oryza sativa by using Bioinformatics Approaches
Abstract Views :395 |
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Authors
Affiliations
1 Department of Plant Science, M. J. P. Rohilkhand University, Bareilly, IN
2 Bioinformatics Centre, IVRI, Izatnagar, Bareilly, IN
3 Department of Biotechnology, Ministry of Science and Technology, New Delhi, IN
1 Department of Plant Science, M. J. P. Rohilkhand University, Bareilly, IN
2 Bioinformatics Centre, IVRI, Izatnagar, Bareilly, IN
3 Department of Biotechnology, Ministry of Science and Technology, New Delhi, IN
Source
Indian Journal of Science and Technology, Vol 6, No 8 (2013), Pagination: 5058-5064Abstract
Thermodynamic properties and nucleic acid symmetry of upstream nucleotide regions to the binding sites, insertion sites or upstream to the gene regions in the eukaryotic genomes are very significant in revealing the properties of various elements such as microRNA sequence, gene sequence and Promoter sequence etc. They set signals for various microRNA to bind at specific location in the genomes and these sequences also help in gene regulation with microRNA. The miR-1 microRNA is one of the most important and common type of small microRNA precursor that regulates its target protein's expression in the cell of both plants and animals therefore its study is very significant. The main emphasis of this research and whole idea behind this kind of work is to find out upstream sequences which carry hidden properties like Railway signals and track sign board which can alert the drivers that how the way is ahead. MicroRNAs are of approximately 21-nucleotides long and play very significant role in gene regulation in variety of genes of different organisms. MicroRNAs and mRNAs (messenger RNA) constitute an important part of gene regulatory networks, influencing diverse biological phenomena. The mRNA (messengerRNA) translated in to protein by the process of translation but when microRNA binds to specific mRNA sequence it alters the gene regulation process this results the alteration in the resulting protein and its function. The binding mechanism of microRNA at target site is not very clear so the study in this field may reveal some secrets which can be helpful to understand the process of binding of microRNA at different mRNA for regulation in different genes. The analysis of upstream nucleotide sequences to the possible binding sites of microRNA in Oryza sativa untranslated mRNA sequences revealed some significant patterns or signals such as thermodynamic properties signals, sequence architectural signals, and energy consideration which may involve directly or indirectly for the binding of microRNA in Oryza sativa.Keywords
PremicroRNA, RicemicroRNAFull Text
References
- Jones-Rhoades M W, Bartel D P et al. (2006). MicroRNAs and their regulatory roles in plants, Annual Review of Plant Biology, vol 57, 19–53.
- Zhang B, Pan X et al. (2006). Computational identification of microRNAs and their targets, Computational Biology and Chemistry, vol 30(6), 395–407.
- Luo Y C, Zhou H et al. (2006). Rice embryogenic calli express a unique set of microRNAs, suggesting regulatory roles of microRNAs in plant post-embryogenic development, FEBS Letters, vol 580(21), 5111–5116.
- Doench J G, and Sharp P A (2004). Specificity of microRNA target selection in translational repression, Genes & Development, vol 18(5), 504–511.
- Wang J F, Zhou H et al. (2004). Identification of 20 microRNAs from Oryza sativa, Nucleic Acids Res., vol 32(5), 1688–1695.
- Khraiwesh B, Asif M A et al. (2010). Transcriptional control of gene expression by MicroRNAs, Cell, vol 140, 111–122.
- Kim S K, Nam J W et al. (2006). microRNA target prediction using support vector machine, BMC Bioinformatics, vol 7, 407–411.
- Schwab R, Palatnik J F et al. (2005). Specific effects of microRNAs on the plant transcriptome, Developmental Cell, vol 8(4), 517–527.
- Pillai R S, Bhattacharyya S N et al. (2007). Repression of protein synthesis by miRNAs: how many mechanisms?, Trends in Cell Biology, vol 17(3), 118–126.
- Rhoads R E (2010). MiRNA Regulation of the Translational Machinery, Springer Press, New York, USA.
- Tang T, Lu J et al. (2006). miRBase: the microRNA sequence database, Methods, Methods in Molecular Biology, vol 342, 129–138.
- Harushima Y, Nakagahra M et al. (2002). Diverse variation of reproductive barriers in three intraspecific rice crosses, Genetics, vol 160(1), 313–322.
- Ma J, and Bennetzen J L (2004). Rapid recent growth and divergence of rice nuclear genomes, Proceedings of the National Academy Sciences, vol 101(34), 12404–12410.
- Grün D, Wang Y L et al. (2005). MicroRNA target predictions across seven Drosophila species and comparison to mammalian targets, PLoS Computional Biology, vol 1(1), e13.
- Lai E C (2003). microRNAs: runts of the genome assert themselves, Current Biology, vol 13(23), R925–R936.
- Ennecke J, Stark A et al. (2005). Principles of microRNA-target recognition, PLOS Biology, vol 3(3), e85.
- Carrington J C, and Ambros V (2003). Role of microRNAs in plant and animal development, Science, vol 301, 336–338.
- Nelson P, Kiriakidou M et al. (2003). The microRNA world: small is mighty, Trends in Biochemical Sciences, vol 28(10), 534–540.
- Bartel D P (2004). MicroRNAs: genomics, biogenesis, mechanism, and function, Cell, vol 116(2), 281–297.
- Wang X J, Reyes J L et al. (2004). Prediction and identification of microRNAs and their mRNA targets, Genome Biology, vol 5(9), R65.
- Ambros V (2004). The functions of animal microRNAs, Nature, vol 431, 350–355.
- Giraldez A J, Mishima Y et al. (2006). Zebrafish MiR-430 promotes deadenylation and clearance of maternal mRNAs, Science, vol 312(5770), 75–79.
- Wu L, Fan J et al. (2006). MicroRNAs direct rapid deadenylation of mRNA, Proceedings of the National Academy Sciences, vol 103, 4034–4039.
- Llave C, Xie Z et al. (2002). Cleavage of Scarecrow-like mRNA targets directed by a class of Arabidopsis microRNA, Science, vol 297, 2053–2056.
- Wang X J, Reyes J L et al. (2004). Prediction and identification of microRNAs and their mRNA targets, Genome Biology, vol 5(9), R65. Epub.
- Sugimoto N, Nakano S et al. (2001). Improved thermodynamic parameters and helix initiation factor to predict stability of DNA duplexes, Nucleic Acids Res., vol. 24(22), 4501–4505.
- Breslauer K J, Frank R et al. (1986). Predicting DNA duplex stability from the base sequence, Proceedings of the National Academy Sciences, vol 83, No. 11, 3746–3750.
- Blake R D (1996). Encyclopedia of Molecular Biology and Molecular Medicine, Wiley-VCH, NY, vol 2, 1–19.
- Altschul S, Gish W et al. (1990). Basic local alignment search tool, Journal of Molecular Biology, vol 215, 403–410.
- Gentleman R (2008). R programming for bioinformatics, Chapman & Hall Press, New York, USA.
- Ouyang S, Zhu W et al. (2007). The TIGR rice genome annotation resource: improvements and new features, Nucleic Acids Research, vol 35, 883–887.
- Wang Y, Li Y et al. (2010). Mechanism of microRNA-target interaction: molecular dynamics simulations and thermodynamics analysis, PLOS Computational Biology, vol 6(7), 860–866.
- Dai X, Zhuang Z et al. (2010). Computational analysis of microRNA targets in plants: current status and challenges, Brief Bioinform, vol 2, 115–121.
- Hofacker I L (2003). Vienna RNA secondary structure server, Nucleic Acids Res., vol 31(13), 3429–3431.
- Kibbe W A (2007). OligoCalc: an online oligonucleotide properties calculator, Nucleic Acids Research, vol 35, W43-6. Epub, 43–46.