Abstract：
BACKGROUND AND OBJECTIVES:Social Network Analysis is an attractive approach to model and analyze complex networks. In recent years, several bioinformatics related networks have been modeled and analyzed thoroughly using social network analysis. The objective of this study is to build a social network analysis based classifier for time sequential data. METHODS:In this work, we model a genomic time sequential data as a 'social' network of interactions. We define interactions as similarity of patients' measurements. Using this 'genomic social network', we develop a classification model called Social Network Analysis-based Classifier. RESULTS:We conducted some experiments to demonstrate how the developed Social Network Analysis-based Classifier outperforms traditional classifiers by effectively classifying a time sequential genomic dataset. Best achieved accuracy is 64.51% and best f-measure is 78.34%. CONCLUSIONS:Our study emphasized Social Network Analysis-based Classifier Model as a powerful technique for analyzing a time sequential dataset. Eventually, the plan is to develop and evolve the Social Network Analysis-based Classifier model into a general classifier.
journal_name
Comput Methods Programs Biomedjournal_title
Computer methods and programs in biomedicineauthors
Üçer S,Koçak Y,Ozyer T,Alhajj Rdoi
10.1016/j.cmpb.2017.06.015subject
Has Abstractpub_date
2017-10-01 00:00:00pages
73-84eissn
0169-2607issn
1872-7565pii
S0169-2607(17)30222-5journal_volume
150pub_type
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