| Chapter title |
T-Cell Epitope Prediction of Chikungunya Virus
|
|---|---|
| Chapter number | 18 |
| Book title |
Chikungunya Virus
|
| Published in |
Methods in molecular biology, May 2016
|
| DOI | 10.1007/978-1-4939-3618-2_18 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-3616-8, 978-1-4939-3618-2
|
| Authors |
Christine Loan Ping Eng, Tin Wee Tan, Joo Chuan Tong |
| Editors |
Justin Jang Hann Chu, Swee Kim Ang |
| Abstract |
There has been a growing demand for vaccines against Chikungunya virus (CHIKV), and epitope-based vaccine is a promising solution. Identification of CHIKV T-cell epitopes is critical to ensure successful trigger of immune response for epitope-based vaccine design. Bioinformatics tools are able to significantly reduce time and effort in this process by systematically scanning for immunogenic peptides in CHIKV proteins. This chapter provides the steps in utilizing machine learning algorithms to train on major histocompatibility complex (MHC) class I peptide binding data and build prediction models for the classification of binders and non-binders. The models could then be used in the identification and prediction of CHIKV T-cell epitopes for future vaccine design. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 6% |
| Unknown | 15 | 94% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 2 | 13% |
| Student > Doctoral Student | 2 | 13% |
| Student > Bachelor | 2 | 13% |
| Researcher | 2 | 13% |
| Professor > Associate Professor | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 6 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 3 | 19% |
| Biochemistry, Genetics and Molecular Biology | 2 | 13% |
| Agricultural and Biological Sciences | 2 | 13% |
| Immunology and Microbiology | 1 | 6% |
| Economics, Econometrics and Finance | 1 | 6% |
| Other | 1 | 6% |
| Unknown | 6 | 38% |