文章基本信息

标题：Utilizing maximal frequent itemsets and social network analysis for HIV data analysis
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作者：Yunuscan Koçak ; Tansel Özyer ; Reda Alhajj 等
期刊名称：Journal of Cheminformatics
印刷版ISSN：1758-2946
电子版ISSN：1758-2946
出版年度：2016
卷号：8
期号：1
页码：71
DOI：10.1186/s13321-016-0184-9
语种：English
出版社：BioMed Central
摘要：Acquired immune deficiency syndrome is a deadly disease which is caused by human immunodeficiency virus (HIV). This virus attacks patients immune system and effects its ability to fight against diseases. Developing effective medicine requires understanding the life cycle and replication ability of the virus. HIV-1 protease enzyme is used to cleave an octamer peptide into peptides which are used to create proteins by the virus. In this paper, a novel feature extraction method is proposed for understanding important patterns in octamer’s cleavability. This feature extraction method is based on data mining techniques which are used to find important relations inside a dataset by comprehensively analyzing the given data. As demonstrated in this paper, using the extracted information in the classification process yields important results which may be taken into consideration when developing a new medicine. We have used 746 and 1625, Impens and schilling data instances from the 746-dataset. Besides, we have performed social network analysis as a complementary alternative method.
关键词：Support Vector Machine ; Feature Selection ; Association Rule ; Betweenness Centrality ; Frequent Itemsets