An Efficient Classifier for Alzheimer’s Disease Genes Identification

doi:10.3390/molecules23123140

中山醫學大學機構典藏 CSMUIR > 健康管理學院 > 應用資訊科學學系暨碩士班 > 期刊論文 > Item 310902500/21062

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题名:	An Efficient Classifier for Alzheimer’s Disease Genes Identification
作者:	Lei Xu;Guangmin Liang;Changrui Liao;Gin-Den Chen;Chi-Chang Chang
贡献者:	應用資訊科學學系暨碩士班
关键词:	Alzheimer’s disease, gene coding protein, sequence information, support vector machine, classification
日期:	2018-11-29
上传时间:	2020-08-10T09:27:55Z (UTC)
出版者:	Molecules.
摘要:	Abstract Alzheimer’s disease (AD) is considered to one of 10 key diseases leading to death in humans. AD is considered the main cause of brain degeneration, and will lead to dementia. It is beneficial for affected patients to be diagnosed with the disease at an early stage so that efforts to manage the patient can begin as soon as possible. Most existing protocols diagnose AD by way of magnetic resonance imaging (MRI). However, because the size of the images produced is large, existing techniques that employ MRI technology are expensive and time-consuming to perform. With this in mind, in the current study, AD is predicted instead by the use of a support vector machine (SVM) method based on gene-coding protein sequence information. In our proposed method, the frequency of two consecutive amino acids is used to describe the sequence information. The accuracy of the proposed method for identifying AD is 85.7%, which is demonstrated by the obtained experimental results. The experimental results also show that the sequence information of gene-coding proteins can be used to predict AD.
URI:	https://ir.csmu.edu.tw:8080/ir/handle/310902500/21062
關聯:	Molecules. 2018 Dec; 23(12): 3140.
显示于类别:	[應用資訊科學學系暨碩士班] 期刊論文

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