Analysis of genetic data in for implementation of compression algorithm in Alzheimer's disease database

A database to store genetic information of Alzheimer's Disease (AD) patients is necessary to spur research in development of drugs suitable for local patients. Works by Abdul Rahman et. al. had created this database. However, the database contains large amounts of redundant genetic data. The st...

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Published in:Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010
Main Author: Yassin I.M.; Abidin H.Z.; Baharom R.; Mat Saat E.H.; Zabidi A.
Format: Conference paper
Language:English
Published: 2010
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955449868&doi=10.1109%2fIECBES.2010.5742291&partnerID=40&md5=08d33edd4705bb37c2b87014f8ef5a32
id 2-s2.0-79955449868
spelling 2-s2.0-79955449868
Yassin I.M.; Abidin H.Z.; Baharom R.; Mat Saat E.H.; Zabidi A.
Analysis of genetic data in for implementation of compression algorithm in Alzheimer's disease database
2010
Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010


10.1109/IECBES.2010.5742291
https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955449868&doi=10.1109%2fIECBES.2010.5742291&partnerID=40&md5=08d33edd4705bb37c2b87014f8ef5a32
A database to store genetic information of Alzheimer's Disease (AD) patients is necessary to spur research in development of drugs suitable for local patients. Works by Abdul Rahman et. al. had created this database. However, the database contains large amounts of redundant genetic data. The storage of uncompressed data is suboptimal because it would consume large storage space as well as slow down transmissions over networks. In this paper, we propose the integration of compression algorithm into the AD genetic database to optimize its storage space. We describe our implementation as well as the results. The compression algorithm had managed to reduce the storage space of the AD database to 38.89% of the original storage space. © 2010 IEEE.


English
Conference paper

author Yassin I.M.; Abidin H.Z.; Baharom R.; Mat Saat E.H.; Zabidi A.
spellingShingle Yassin I.M.; Abidin H.Z.; Baharom R.; Mat Saat E.H.; Zabidi A.
Analysis of genetic data in for implementation of compression algorithm in Alzheimer's disease database
author_facet Yassin I.M.; Abidin H.Z.; Baharom R.; Mat Saat E.H.; Zabidi A.
author_sort Yassin I.M.; Abidin H.Z.; Baharom R.; Mat Saat E.H.; Zabidi A.
title Analysis of genetic data in for implementation of compression algorithm in Alzheimer's disease database
title_short Analysis of genetic data in for implementation of compression algorithm in Alzheimer's disease database
title_full Analysis of genetic data in for implementation of compression algorithm in Alzheimer's disease database
title_fullStr Analysis of genetic data in for implementation of compression algorithm in Alzheimer's disease database
title_full_unstemmed Analysis of genetic data in for implementation of compression algorithm in Alzheimer's disease database
title_sort Analysis of genetic data in for implementation of compression algorithm in Alzheimer's disease database
publishDate 2010
container_title Proceedings of 2010 IEEE EMBS Conference on Biomedical Engineering and Sciences, IECBES 2010
container_volume
container_issue
doi_str_mv 10.1109/IECBES.2010.5742291
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-79955449868&doi=10.1109%2fIECBES.2010.5742291&partnerID=40&md5=08d33edd4705bb37c2b87014f8ef5a32
description A database to store genetic information of Alzheimer's Disease (AD) patients is necessary to spur research in development of drugs suitable for local patients. Works by Abdul Rahman et. al. had created this database. However, the database contains large amounts of redundant genetic data. The storage of uncompressed data is suboptimal because it would consume large storage space as well as slow down transmissions over networks. In this paper, we propose the integration of compression algorithm into the AD genetic database to optimize its storage space. We describe our implementation as well as the results. The compression algorithm had managed to reduce the storage space of the AD database to 38.89% of the original storage space. © 2010 IEEE.
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