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Feature Fusion Based SVM Classifier for Protein Subcellular Localization Prediction

Rahman, Julia ; Mondal, Nazrul Islam ; Islam, Khaled Ben ; Hasan, Al Mehedi

Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516)


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Abstract:
For the importance of protein subcellular localization in different branch of life science and drug discovery, researchers have focused their attentions on protein subcellular localization prediction. Effective representation of features from protein sequences plays most vital role in protein subcellular localization prediction specially in case of machine learning technique. Single feature representation like pseudo amino acid composition (PseAAC), physiochemical property model (PPM), amino acid index distribution (AAID) contains insufficient information from protein sequences. To deal with such problem, we have proposed two feature fusion representations AAIDPAAC and PPMPAAC to work with Support Vector Machine classifier, which fused PseAAC with PPM and AAID accordingly. We have evaluated performance for both single and fused feature representation of Gram-negative bacterial dataset. We have got at least 3% more actual accuracy by AAIDPAAC and 2% more locative accuracy by PPMPAAC than single feature representation.


Institution: Faculty of Technology, Research Groups in Informatics
DDC classification: Data processing, computer science, computer systems

Suggested Citation:
Rahman, Julia ; Mondal, Nazrul Islam ; Islam, Khaled Ben ; Hasan, Al Mehedi  (2016)  Feature Fusion Based SVM Classifier for Protein Subcellular Localization Prediction. Journal of Integrative Bioinformatics - JIB (ISSN 1613-4516), 13(1), 2016

Online-Journal: http://journal.imbio.de/article.php?aid=288
URL: http://biecoll.ub.uni-bielefeld.de/volltexte/2017/5430



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