Name
Title | ||
|---|---|---|
HLAB: learning the BiLSTM features from the ProtBert-encoded proteins for the class I HLA-peptide binding prediction |
Abstract | ||
|---|---|---|
Human Leukocyte Antigen (HLA) is a type of molecule residing on the surfaces of most human cells and exerts an essential role in the immune system responding to the invasive items. The T cell antigen receptors may recognize the HLA-peptide complexes on the surfaces of cancer cells and destroy these cancer cells through toxic T lymphocytes. The computational determination of HLA-binding peptides will facilitate the rapid development of cancer immunotherapies. This study hypothesized that the natural language processing-encoded peptide features may be further enriched by another deep neural network. The hypothesis was tested with the Bi-directional Long Short-Term Memory-extracted features from the pretrained Protein Bidirectional Encoder Representations from Transformers-encoded features of the class I HLA (HLA-I)-binding peptides. The experimental data showed that our proposed HLAB feature engineering algorithm outperformed the existing ones in detecting the HLA-I-binding peptides. The extensive evaluation data show that the proposed HLAB algorithm outperforms all the seven existing studies on predicting the peptides binding to the HLA-A*01:01 allele in AUC and achieves the best average AUC values on the six out of the seven k-mers (k=8,9,...,14, respectively represent the prediction task of a polypeptide consisting of k amino acids) except for the 9-mer prediction tasks. The source code and the fine-tuned feature extraction models are available at http://www.healthinformaticslab.org/supp/resources.php. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1093/BIB/BBAC173 | Briefings in Bioinformatics |
Keywords | DocType | Volume |
BERT,BiLSTM,bioinformatics,class I HLA-binding peptide prediction,feature selection,natural language processing | Journal | 23 |
Issue | ISSN | Citations |
5 | 1477-4054 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaqi Zhang | 1 | 0 | 0.34 |
| Gancheng Zhu | 2 | 0 | 0.34 |
| Kewei Li | 3 | 0 | 0.34 |
| Li Fei | 4 | 0 | 6.76 |
| Huang Lan | 5 | 10 | 13.31 |
| Meiyu Duan | 6 | 0 | 4.06 |
| Fengfeng Zhou | 7 | 49 | 8.28 |