Social network Analysis-based classifier (SNAc): A case study on time course gene expression data. (October 2017)

• Record Type: Journal Article
• Authors: Üçer, Serkan; Koçak, Yunuscan; Ozyer, Tansel; Alhajj, Reda
• Is Part Of: Computer methods and programs in biomedicine. Volume 150(2017)
• Page Start: 73
• Page End: 84
• Publication Date: 2017-10
• Subjects: Genomic interactions -- Social network analysis -- Social network classifier -- Gene expression data; TCGE Time Course Gene Expression -- SVM Support Vector Machine -- SVMRFE Support Vector Machine Recursive Feature Elimination -- SNAc Social Network Analysis based Classifier -- HITS Hyper-Induced Topic Search -- MBM Minimize Block Model; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.06.015
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A genomic time sequential data has been realized as a 'social' network of interactions. We define. Interactions are realized based on similarity of patients' measures. Developed a classification model named as Social Network Analysis-based Classifier (SNAc). The conducted testing demonstrate that SNAc outperformed traditional classifiers by effectively classifying a time sequential genomic dataset. 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.