NDLI: Solving Haplotype Reconstruction Problem in MEC Model with Hybrid Information Fusion

Content Provider	IEEE Xplore Digital Library
Author	Asgarian, E. Moeinzadeh, M.-H. Habibi, J. Sharifian-R, S. Rasooli-V, A. Najafi-A, A.
Copyright Year	2008
Description	Author affiliation: Dept. of Comput. Eng., Sharif Univ. of Technol., Tehran (Asgarian, E.)
Abstract	Single nucleotide polymorphisms (SNPs), a single DNA base varying from one individual to another, are believed to be the most frequent form responsible for genetic differences. Genotype is the conflated information of a pair of haplotypes on homologous chromosomes. Although haplotypes have more information for disease associating than individual SNPs and genotype, it is substantially more difficult to determine haplotypes through experiments. Hence, computational methods which can reduce the cost of determining haplotypes become attractive alternatives. MEC, as a standard model for haplotype reconstruction, is fed by fragments as input to infer the best pair of haplotypes with minimum error to be corrected. It is proved that haplotype reconstruction in MEC model is a NP-Hard problem. Thus, reducing running time and obtaining acceptable result are desired by researchers. Heuristic algorithms and different clustering methods are employed to achieve these goals. In this paper, the idea of combining different methods is presented. A hybrid model, which is employed the efficiency of different serial and parallel models, is suggested. FCA, K-means and neural network are considered as its component. K-means clustering method is used to improve neural network efficiency. Then the results are compared in different datasets.
Starting Page	214
Ending Page	218
File Size	311242
Page Count	5
File Format	PDF
ISBN	9780769533254
DOI	10.1109/EMS.2008.97
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-08
Publisher Place	UK
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Clustering methods Biological system modeling Clustering algorithms Artificial neural networks Classification algorithms Bioinformatics
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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