Training and applying Training and applying hidden Markov models and hidden Markov models and support vector machines support vector machines for prediction of T-cell for prediction of T-cell epitopes epitopes Van Hai Van Van Hai Van , Cao Thi Ngoc Phuong, Tran Linh , Cao Thi Ngoc Phuong, Tran Linh Thuoc Thuoc Faculty of Biology, University of Natural Faculty of Biology, University of Natural Sciences, Sciences, VNU-HCMC, Vietnam VNU-HCMC, Vietnam Sixth International Sixth International Conference on Conference on Bioinformatics Bioinformatics InCoB2007 InCoB2007
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Training and applying hidden Markov models and support vector machines for prediction of T-cell epitopes Van Hai Van, Cao Thi Ngoc Phuong, Tran Linh Thuoc.
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Training and applying Training and applying hidden Markov models and hidden Markov models and support vector machines support vector machines
for prediction of T-cell epitopesfor prediction of T-cell epitopes
Van Hai VanVan Hai Van, Cao Thi Ngoc Phuong, Tran Linh Thuoc, Cao Thi Ngoc Phuong, Tran Linh Thuoc
Faculty of Biology, University of Natural Sciences, Faculty of Biology, University of Natural Sciences, VNU-HCMC, VietnamVNU-HCMC, Vietnam
Sixth InternationalSixth InternationalConference on BioinformaticsConference on Bioinformatics
InCoB2007InCoB2007
Epitope predictionEpitope prediction
“Epitope is the portion of an antigen that is recognized by the antigen receptor on lymphocytes”
Molecular Biology
Epitope prediction:
Computers aid to develop epitope-based vaccines against various human pathogens for which no vaccines currently exist
T-cell epitope predictionT-cell epitope prediction •T-cell epitopes are a subset of MHC binding peptides prediction of the peptides binding to MHC is essential for design of peptide-based vaccines•HLA-A0201
Sequence
Binding motifs
Quantitative matrices
Decision tree
Artificial neural networks
Hidden Markov models
Support vector machines
Molecular Biology
HMMs & SVMsHMMs & SVMs
HMMs
(Hidden Markov Models)
Statistical model that can capture complex relationships in data sets.
SVMs
(Support Vector Machines):
Learning machine that can find the optimal separating hyperplane.
Epitope prediction for dengue virusEpitope prediction for dengue virusTropical disease• Dengue fever• Dengue hemorraghic fever• Dengue shock syndromeHypothesis of pathogenesis• Antibody – dependent
enhancement• Virus virulenceNo dengue vaccine is available
In our research:
. Develop procedure for building automatically T-cell epitope predicting models
. Find candidates in silico for making multivalent vaccines on 4 types of Dengue virus
Building models for predicting T-cell epitopes Building models for predicting T-cell epitopes & applying these models on dengue virus& applying these models on dengue virus
Building effective prediction models?Building effective prediction models?
The predicting ability of HMM and SVM models depends on:
•Experimentally peptides binding to MHC molecules
•Partition of the peptides into training set and testing set
•Encoding method
A system finds easily and quickly the best prediction model when type of MHC molecules and quantity of binding peptides are changed