Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1178
Title: Utilizing maximal frequent itemsets and social network analysis for HIV data analysis
Authors: Koçak, Yunuscan
Özyer, Tansel
Alhajj, Reda
143116
Keywords: Viruses
Wind
HIV-1 protease
Issue Date: 9-Dec-2016
Publisher: Biomed Central Ltd.
Source: Koçak, Y., Özyer, T., & Alhajj, R. (2016). Utilizing maximal frequent itemsets and social network analysis for HIV data analysis. Journal of cheminformatics, 8(1), 71.
Abstract: 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.
URI: https://jcheminf.biomedcentral.com/articles/10.1186/s13321-016-0184-9
https://hdl.handle.net/20.500.11851/1178
ISSN: 1758-2946
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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