Beschreibung
An integrated methodology is proposed to combine the major areas of protein analysis. It can be considered as a guideline that can be used to analyze and model a protein. This methodology is applied on one of proteins' viruses for analysis and modeling; non-structural protein 5a (NS5a) of Hepatitis C virus (HCV). Also, SemiBoost-Fold Recognition (SB-FR) algorithm is proposed for predicting protein fold. SB-FR proposes a semi-supervised boosting combination to achieve better multi-class classification model. A famous challengeable dataset (Ding and Dubchak dataset) is used for training and testing this proposed algorithm. Moreover, "TreeTest" testing method is introduced for improving the overall accuracy of SB-FR algorithm with lower computational time.
Autorenporträt
She received her Ph.D. in Computer Science, in 2012. She is currently Assistant professor in faculty of Computers and Information, Helwan University, Cairo, Egypt. Her research interests include structural bioinformatics, pattern recognition, machine learning, and image processing.
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BoD - Books on Demand
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