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An Efficient Bioinformatics Processing Scheme using AHP Algorithm in Big Data Environment
Background/Objectives: Bioinformatic information in many diverse areas is being collected, managed, and stored in relation to the genome project. However, studies related to bioinformatics until now has very low efficiency due to improper management of the bioinformatic information. Methods/Statistical Analysis: In this paper, a data processing scheme applying the AHP algorithm is proposed for efficient management of bioinformatic information. The proposed scheme improves information accuracy by assigning property weights by hierarchically classifying the property information (type, property, priority, etc). Findings: Furthermore, the proposed scheme interconnects weighted bioinformatic information according to the weight to minimize the process time between the server and user, thereby improving the information accessibility. Application/Improvements: As a result of performance evaluation, the proposed scheme obtained improved results compared to the conventional schemes in terms of throughput and process time between the LSS (Location Service Server) and user.
AHP, Algorithm, Big Data, Bioinformatics, Data Process.
- Disz T, Kubal M, Olson R, Overbeek R, Stevens R. Challenges in large scale distributed computing: Bioinformatics. Proceedings Challenges of Large Applications in Distributed Environments; Research Triangle Park, NC. 2005. p. 57–65.
- Sumitomo J, Hogan JM, Newell F, Roe P. BioMashups: The new world of exploratory bioinformatics? Proceedings of IEEE 4th International Conference on eScience; Indiana, USA. 2008. p. 422–3.
- Lengauer T. Algorithmic research problems in molecular bioinformatics. Proceedings of the 2nd Israel Symposium on the Theory and Computing Systems; Natanya, Israel. 1993. p. 177–92.
- Alterovitz G, Ramoni MF. Bioinformatics and proteomics: An engineering problem solving-based approach. IEEE Transactions on Education. 2007 Feb; 50(1):49–54.
- Saaty TL. How to make a decision: The analytic hierarchy process. European Journal of Operational Research. 1990 Sep; 48(1): 9–26.
- Neelakanta P, Chatterjee S, Pappusetty D, Pavlovic M. Information-theoretic algorithms in bioinformatics and bio-/medical-imaging: A review. Proceedings of 2011 ICRTIT; Chennai, India. 2011. p. 183–8.
- Lau KW, Siepen J. Bioinformatic approaches to improve the identification of peptides from proteomics experiments. Proceedings of the Institution of Engineering and Technology Seminar on Signal Processing for Genomics; Cambridge, UK. 2006. p. 23–45.
- Jeong YS, Lee BK, Lee SH. An efficient device authentication protocol using bioinformatic. Processing of 2006 International Conference on Computational Intelligence and Security; Guangzhou, China. 2006. p. 855–8.
- Wang RS, Wu LY, Li ZP, Zhang XS. Haplotype reconstruction from SNP fragments by minimum error correction. Bioinformatics. 2005 Feb; 21(10):2456–62.
- Wang Y, Feng E, Ruisheng RW. A clustering algorithm based on two distance functions for MEC model. Computational Biology and Chemistry. 2007 Apr; 31(2):148–50.
- Bustamam A, Burrage K, Hamilton NA. Fast parallel markov clustering in bioinformatics using massively parallel computing on GPU with CUDA and ELLPACK-R sparse format. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2012 May; 9(3):679–92.
- Chuang EY. Combination of high-throughput genomic technologies and bioinformatics for molecular characterization of cancer. Proceedings of 2013 3rd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME); Bandung. 2013. p. 1.
- Qian W, Yang Y, Yang N, Li C. Particle swarm optimization for SNP haplotype reconstruction problem. Applied Mathematics and Computation. 2007 Feb; 196(1):266–72.
- Sun X, Fan L, Yan L, Kong L, Ding Y, Guo C, Sun W. Deliver Bioinformatics Services in Public Cloud: Challenges and Research Framework. Proceedings of 2011 IEEE 8th International Conference on e-Business Engineering (ICEBE); Beijing, China. 2011. p. 352–7.
- Jeong YS, Kim YT, Park GC. Data security scheme for multiple attribute information in big data environment. Indian Journal of Science and Technology. 2015 Sep; 8 (24):1–7.
- Jeong YS. Parallel processing scheme for minimizing computational and communication cost of bioinformatics data. Indian Journal of Science and Technology. 2015 Jul; 8(15):1–8.
- Mazari AA. Bioinformatics and healthcare computing models and services on grid initiatives for data analysis and management. Proceedings of 2014 3rd International Conference on Advanced Computer Science Applications and Technologies (ACSAT); Amman. 2014. p. 26–31.
- Yun SY, Min SH. a fault-tolerant bootstrap server for a system with a very large number of personal healthcare devices. Indian Journal of Science and Technology. 2015 Oct; 8(25):1–6.
- Lee SR. Medical information security analysis for standardization strategy in Korea. Indian Journal of Science and Technology. 2015 Oct; 8(25):1–7.
- Roman R, Zhou J, Lopez J. Feed-forward artificial neural network based inference system applied in bioinformatics data-mining. Proceedings of IJCNN International Joint Conference on Neural Networks; Atlanta, Georgia. 2009. p. 1744–9.
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