Prediction of CTL epitopes using QM, SVM and ANN techniques
Vaccine, ISSN: 0264-410X, Vol: 22, Issue: 23, Page: 3195-3204
2004
- 333Citations
- 221Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations333
- Citation Indexes333
- 333
- CrossRef254
- Captures221
- Readers221
- 221
Article Description
Cytotoxic T lymphocyte (CTL) epitopes are potential candidates for subunit vaccine design for various diseases. Most of the existing T cell epitope prediction methods are indirect methods that predict MHC class I binders instead of CTL epitopes. In this study, a systematic attempt has been made to develop a direct method for predicting CTL epitopes from an antigenic sequence. This method is based on quantitative matrix (QM) and machine learning techniques such as Support Vector Machine (SVM) and Artificial Neural Network (ANN). This method has been trained and tested on non-redundant dataset of T cell epitopes and non-epitopes that includes 1137 experimentally proven MHC class I restricted T cell epitopes. The accuracy of QM-, ANN- and SVM-based methods was 70.0, 72.2 and 75.2%, respectively. The performance of these methods has been evaluated through Leave One Out Cross-Validation (LOOCV) at a cutoff score where sensitivity and specificity was nearly equal. Finally, both machine-learning methods were used for consensus and combined prediction of CTL epitopes. The performances of these methods were evaluated on blind dataset where machine learning-based methods perform better than QM-based method. We also demonstrated through subgroup analysis that our methods can discriminate between T-cell epitopes and MHC binders (non-epitopes). In brief this method allows prediction of CTL epitopes using QM, SVM, ANN approaches. The method also facilitates prediction of MHC restriction in predicted T cell epitopes. The method is available at http://www.imtech.res.in/raghava/ctlpred/.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0264410X04001409; http://www.imtech.res.in/raghava/ctlpred/; http://dx.doi.org/10.1016/j.vaccine.2004.02.005; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=3843052528&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/15297074; https://linkinghub.elsevier.com/retrieve/pii/S0264410X04001409; https://dx.doi.org/10.1016/j.vaccine.2004.02.005
Elsevier BV
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