Chapter title |
MetaMHCpan, A Meta Approach for Pan-Specific MHC Peptide Binding Prediction.
|
---|---|
Chapter number | 49 |
Book title |
Vaccine Design
|
Published in |
Methods in molecular biology, January 2016
|
DOI | 10.1007/978-1-4939-3389-1_49 |
Pubmed ID | |
Book ISBNs |
978-1-4939-3388-4, 978-1-4939-3389-1
|
Authors |
Yichang Xu, Cheng Luo, Hiroshi Mamitsuka, Shanfeng Zhu |
Editors |
Sunil Thomas |
Abstract |
Recent computational approaches in bioinformatics can achieve high performance, by which they can be a powerful support for performing real biological experiments, making biologists pay more attention to bioinformatics than before. In immunology, predicting peptides which can bind to MHC alleles is an important task, being tackled by many computational approaches. However, this situation causes a serious problem for immunologists to select the appropriate method to be used in bioinformatics. To overcome this problem, we develop an ensemble prediction-based Web server, which we call MetaMHCpan, consisting of two parts: MetaMHCIpan and MetaMHCIIpan, for predicting peptides which can bind MHC-I and MHC-II, respectively. MetaMHCIpan and MetaMHCIIpan use two (MHC2SKpan and LApan) and four (TEPITOPEpan, MHC2SKpan, LApan, and MHC2MIL) existing predictors, respectively. MetaMHCpan is available at http://datamining-iip.fudan.edu.cn/MetaMHCpan/index.php/pages/view/info . |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Peru | 1 | 14% |
Unknown | 6 | 86% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 2 | 29% |
Professor | 1 | 14% |
Student > Bachelor | 1 | 14% |
Student > Doctoral Student | 1 | 14% |
Unknown | 2 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 2 | 29% |
Biochemistry, Genetics and Molecular Biology | 1 | 14% |
Computer Science | 1 | 14% |
Neuroscience | 1 | 14% |
Unknown | 2 | 29% |