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Yasser EL-Manzalawy
Researcher at Penn State College of Information Sciences and Technology
Publications - 46
Citations - 1727
Yasser EL-Manzalawy is an academic researcher from Penn State College of Information Sciences and Technology. The author has contributed to research in topics: Feature selection & Epitope. The author has an hindex of 14, co-authored 46 publications receiving 1377 citations. Previous affiliations of Yasser EL-Manzalawy include Geisinger Health System & Iowa State University.
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Journal ArticleDOI
Predicting linear B-cell epitopes using string kernels.
TL;DR: BCPred, a novel method for predicting linear B‐cell epitopes using the subsequence kernel, is proposed and it is shown that the predictive performance of BCPred outperforms 11 SVM‐based classifiers developed and evaluated in the authors' experiments as well as the implementation of AAP (AUC = 0.7).
Proceedings ArticleDOI
Predicting flexible length linear B-cell epitopes.
TL;DR: FBCPred, a novel method for predicting flexible length linear B-cell epitopes using the subsequence kernel is proposed and demonstrated that FBCPred significantly outperforms all other classifiers evaluated in this study.
Journal ArticleDOI
Recent advances in B-cell epitope prediction methods.
TL;DR: Recent advances in computational methods for B-cell epitope prediction are reviewed, some gaps in the current state of the art are identified, and some promising directions for improving the reliability of such methods are outlined.
Journal ArticleDOI
Predicting protein-protein interface residues using local surface structural similarity
Rafael A. Jordan,Rafael A. Jordan,Yasser EL-Manzalawy,Yasser EL-Manzalawy,Drena Dobbs,Vasant Honavar +5 more
TL;DR: A comparison of PrISEC with PredUs, a recently developed method for predicting interface residues of a query protein based on the known interfaces of its (global) structural homologs, shows that performance superior or comparable to that of PredUs can be obtained using only local surface structural similarity.
Journal ArticleDOI
RNABindRPlus: a predictor that combines machine learning and sequence homology-based methods to improve the reliability of predicted RNA-binding residues in proteins.
Rasna R. Walia,Li C. Xue,Katherine Wilkins,Yasser EL-Manzalawy,Drena Dobbs,Vasant Honavar,Vasant Honavar +6 more
TL;DR: Two novel approaches to identify putative RNA-binding residues in proteins using sequence homology-based methods and a new method that combines predictions from HomPRIP with those from an optimized Support Vector Machine (SVM) classifier trained on a benchmark dataset of 198 RNA- binding proteins are reported.