Asian Journal of Information Technology

Year: 2017
Volume: 16
Issue: 8
Page No. 675 - 685

Data Driven Approach for Genetic Disorder Prediction by Aggregating Mutational Features

Authors : Sathyavikasini Kalimuthu and Vijaya Vijayakumar

References

Agnes, J.A.M.D., K.M.S. Karen and E.S.M.D. Michael, 2008. Charcot-Marie-Tooth Neuropathies: Diagnosis and Management. Thieme Medical Publishers, Stuttgart, Germany,.

Ban, H.J., J.Y. Heo, K.S. Oh and K.J. Park, 2010. Identification of type 2 diabetes-associated combination of SNPs using support vector machine. BMC. Genet., 11: 26-26.
CrossRef  |  PubMed  |  Direct Link  |  

Bennett, R.R., H.E. Schneider, E. Estrella, S. Burgess and A.S. Cheng et al., 2009. Automated DNA mutation detection using universal conditions direct sequencing: Application to ten muscular dystrophy genes. BMC Genet., 10: 66-66.
CrossRef  |  PubMed  |  Direct Link  |  

Briggs, F.B.S., P.P. Ramsay, E. Madden, J.M. Norris and V.M. Holers et al., 2010. Supervised machine learning and logistic regression identifies novel epistatic risk factors with PTPN22 for rheumatoid arthritis. Genes Immune., 11: 199-208.
CrossRef  |  Direct Link  |  

Cartegni, L., J. Wang, A. Zhu, M.Q. Zhang abd A.R. Krainer, 2003. ESEfinder: A web resource to identify exonic enhancers. Nucl. Acid Res., 31: 3568-3571.
PubMed  |  

Chen, C., H. Ma, F. Zhang, L. Chen and X. Xing et al., 2014. Screening of Duchenne Muscular Dystrophy (DMD) mutations and investigating its mutational mechanism in Chinese patients. PloS One, Vol. 9,

Clancy, S., 2008. RNA splicing: Introns, exons and spliceosome. Nat. Educ., 1: 31-31.
Direct Link  |  

Desmet, F.O., D. Hamroun, M. Lalande, G.C. Beroud and M. Claustres et al., 2009. Human splicing finder: An online bioinformatics tool to predict splicing signals. Nucleic Acids Res., 37: e67-e67.
PubMed  |  Direct Link  |  

Emery, A.E., 2002. The muscular dystrophies. Lancet, 359: 687-695.
PubMed  |  Direct Link  |  

Goh, K.I. and I.G. Choi, 2012. Exploring the human diseasome: The human disease network. Briefings Funct. Genomics, 11: 533-542.
PubMed  |  Direct Link  |  

Gonzalez, N.F.F., L.A.B. Munoz and K.A.S. Colon, 2013. Effective classification and gene expression profiling for the facioscapulohumeral muscular dystrophy. PloS one, Vol. 8,

Kalari, K.R., 2006. Computational approach to identify deletions or duplications within a gene. Ph.D Thesis, University of Iowa, Iowa City, Iowa.

Kann, M.G., 2010. Advances in translational bioinformatics: Computational approaches for the hunting of disease genes. Briefings Bioinf., 11: 96-110.
PubMed  |  Direct Link  |  

Koenig, M., E.P. Hoffman, C.J. Bertelson, A.P. Monaco and C. Feener et al., 1987. Complete cloning of the Duchenne Muscular Dystrophy (DMD) cDNA and preliminary genomic organization of the DMD gene in normal and affected individuals. Cell, 50: 509-517.
PubMed  |  Direct Link  |  

Lim, K.H. and W.G. Fairbrother, 2012. Spliceman-a computational web server that predicts sequence variations in pre-mRNA splicing. Bioinf., 28: 1031-1032.
Direct Link  |  

Ma, J., M.N. Nguyen, G.W. Pang and J.C. Rajapakse, 2005. Gene classification using codon usage and SVMS. Proceedings of the 2005 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB'05), November 15, 2005, IEEE, Singapore, Asia, ISBN:0-7803-9387-2, pp: 1-8.

Mercuri, E. and F. Muntoni, 2013. Muscular dystrophies. Lancet, 381: 845-860.
PubMed  |  Direct Link  |  

Mort, M., T.S. Weiler, B. Li, E.V. Ball and D.N. Cooper et al., 2014. MutPred splice: Machine learning-based prediction of exonic variants that disrupt splicing. Genome Biol., Vol. 15, 10.1186/gb-2014-15-1-r19

Nicodemus, K.K., J.H. Callicott, R.G. Higier, A. Luna and D.C. Nixon et al., 2010. Evidence of statistical epistasis between DISC1, CIT and NDEL1 impacting risk for schizophrenia: Biological validation with functional neuroimaging. Hum. Genet., 127: 441-452.
CrossRef  |  Direct Link  |  

Nisha, C.M., B. Pant and K.R. Pardasani, 2012. SVM model for classification of genotypes of HCV using relative synonymous codon usage. J. Adv. Bioinf. Appl. Res., 3: 357-363.

Noguchi, S., T. Tsukahara, M. Fujita, R. Kurokawa and M. Tachikawa et al., 2003. cDNA microarray analysis of individual Duchenne muscular dystrophy patients. Hum. Mol. Genet., 12: 595-600.
Direct Link  |  

Reese, M.G., F.H. Eeckman, D. Kulp and D. Haussler, 1997. Improved splice site detection in Genie. J. Comput. Boil., 4: 311-323.
CrossRef  |  PubMed  |  Direct Link  |  

Roberts, R.G., M.A.R.T.I.N. Bobrow and D.R. Bentley, 1992. Point mutations in the dystrophin gene. Proc. National Acad. Sci., 89: 2331-2335.
Direct Link  |  

Sathyavikashini, K. and M.S. Vijaya, 2015. Predicting muscular dystrophy through genetic testing: A study. Proceedings of the International Conference on Innovative Trends in Electronics Communication and Applications (ICIECA 2015), December 19-20, 2015, Indian Institute of Technology Madras, Chennai, India, pp: 64-71.

Sathyavikasini, K. and M.S. Vijaya, 2015. Predicting muscular dystrophy with sequence based features for point mutations. Proceedings of the 2015 IEEE International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN), November 20-22, 2015, IEEE, Coimbatore, India, ISBN:978-1-4673-6734-9, pp: 235-240.

Sathyavikasini, K. and M.S. Vijaya, 2016. Muscular dystrophy disease classification using relative synonymous codon usage. Intl. J. Mach. Learn. Comput., 6: 139-144.
Direct Link  |  

Tranchevent, L.C., F.B. Capdevila, D. Nitsch, B.D. Moor and P.D. Causmaecker et al., 2011. A guide to web tools to prioritize candidate genes. Briefings Bioinf., 12: 22-32.
PubMed  |  Direct Link  |  

Uhmn, S., D.H. Kim, Y.W. Ko, S. Cho and J. Cheong et al., 2009. A study on application of single nucleotide polymorphism and machine learning techniques to diagnosis of chronic hepatitis. Expert Syst., 26: 60-69.
CrossRef  |  Direct Link  |  

Woolfe, A., J.C. Mullikin and L. Elnitski, 2010. Genomic features defining exonic variants that modulate splicing. Genome Boil., Vol. 11, 10.1186/gb-2010-11-2-r20

Wu, J., W. Zhang and R. Jiang, 2010. Comparative study of ensemble learning approaches in the identification of disease mutations. Proceedings of the 3rd International Conference on Biomedical Engineering and Informatics (BMEI) 2010, Vol. 6, October 16-18, 2010, IEEE, Beijing, China, ISBN:978-1-4244-6495-1, pp: 2306-2310.

Yeo, G. and C.B. Burge, 2004. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comput. Biol., 11: 377-394.
CrossRef  |  PubMed  |  Direct Link  |  

Zeng, S., J. Yang, B.H.Y. Chung, Y.L. Lau and W. Yang, 2014. EFIN: Predicting the functional impact of nonsynonymous single nucleotide polymorphisms in human genome. BMC Genomics, 15: 455-455.
CrossRef  |  Direct Link  |  

Design and power by Medwell Web Development Team. © Medwell Publishing 2022 All Rights Reserved