Gene Classification Using Codon Usage and Support Vector Machines
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Di-codon Usage for Gene Classification
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
A novel kernel-based approach for predicting binding peptides for HLA class II molecules
ISBRA'07 Proceedings of the 3rd international conference on Bioinformatics research and applications
A hybrid model for prediction of peptide binding to MHC molecules
ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Predicting MHC-II Binding Affinity Using Multiple Instance Regression
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Leveraging information across HLA alleles/supertypes improves epitope prediction
RECOMB'06 Proceedings of the 10th annual international conference on Research in Computational Molecular Biology
Artificial Intelligence in Medicine
| Hi-index | 3.84 |
Summary: Prediction of peptides binding with MHC class II allele HLA-DRB1*0401 can effectively reduce the number of experiments required for identifying helper T cell epitopes. This paper describes support vector machine (SVM) based method developed for identifying HLA-DRB1*0401 binding peptides in an antigenic sequence. SVM was trained and tested on large and clean data set consisting of 567 binders and equal number of non-binders. The accuracy of the method was 86% when evaluated through 5-fold cross-validation technique. Available: A web server HLA-DR4Pred based on above approach is available at http://www.imtech.res.in/raghava/hladr4pred/ and http://bioinformatics.uams.edu/mirror/hladr4pred/ (Mirror Site). Supplementary information: http://www.imtech.res.in/raghava/hladr4pred/info.html