Analysis of genetic data in for implementation of compression algorithm in Alzheimer's disease database - Zainol Abidin, Husna; M. Yassin, Ihsan; Abdul Rahman, Farah Yasmin (2011)
Alzheimer's Disease (AD) is a progressive and degenerative brain disease that causes serious impairment in cognition and memory [2]. When brain cells degenerate and die, the brain markedly shrink in specific regions [3,4]; namely the medial temporal (limbic), posterior cingulate gyrus and in the lateral hemisphere [3]. As AD progresses and affects different areas of the brain, various neural functions become impaired. The result is irreversible changes in ability and/or behavior of the patient. The number of people living with AD in 2006 was 26.6 million [5]. Research by [5] estimate that by 2050, AD prevalence will quadruple by which time 1 in 85 persons worldwide will be afflicted by the disease. Undergoing research is trying to develop interventions to both delay the onset of AD, as well as to slow the progression of the disease. Effective interventions may significantly minimize the prevalence and incidence of AD, improve the quality of life for patients and their caregivers, and reduce the resources needed to provide adequate institutional and home health care [16]. Research in AD involves analysis of large volumes of data, perhaps over diverse locations. Due to its massive volume, efficient storage of the data is paramount. This data must also be easily accessible to all users that require them. The emergence of Web-Driven Database Applications (WDDA) has helped fulfil this requirement. Data is organized in databases for efficient storage and retrieval, while this information is made available to a wide audience through the usage of the Internet. Currently, research on AD utilizes patients' databases from foreign countries [6-9]. This may lead to drugs that are unsuitable for Malaysian patients, since an important concern for drug discovery is the effectiveness of the drugs may differ in individuals with different genetic information [10]. Since Malaysia is a multi-ethnic country comprising of people from Malay, Chinese and Indian descent, a database containing medical information of Malaysia AD patients would stimulate research in drugs that are tailored to Malaysian patients. Since genetic data require massive amounts of storage, there is an issue regarding the size of the database and the efficiency of transmission over networks. This would cause database searches and transmissions over networks to become extremely slow, thus affecting the effectiveness of the database. Therefore, we propose an extension to [1], where we will incorporate a compression algorithm to the database. The incorporation of the compression algorithm would significantly reduce the size of the database (making future database searches faster), as well as increasing the transmission efficiency over the network. In this paper, we present the initial analysis of the data to identify candidates for compression, as well as implement the proposed compression algorithm to those data.