PMID- 37993812
OWN - NLM
STAT- MEDLINE
DCOM- 20231127
LR  - 20231127
IS  - 1471-2164 (Electronic)
IS  - 1471-2164 (Linking)
VI  - 24
IP  - 1
DP  - 2023 Nov 23
TI  - DeepHLAPred: a deep learning-based method for non-classical HLA binder 
      prediction.
PG  - 706
LID - 10.1186/s12864-023-09796-2 [doi]
LID - 706
AB  - Human leukocyte antigen (HLA) is closely involved in regulating the human immune 
      system. Despite great advance in detecting classical HLA Class I binders, there 
      are few methods or toolkits for recognizing non-classical HLA Class I binders. To 
      fill in this gap, we have developed a deep learning-based tool called 
      DeepHLAPred. The DeepHLAPred used electron-ion interaction pseudo potential, 
      integer numerical mapping and accumulated amino acid frequency as initial 
      representation of non-classical HLA binder sequence. The deep learning module was 
      used to further refine high-level representations. The deep learning module 
      comprised two parallel convolutional neural networks, each followed by maximum 
      pooling layer, dropout layer, and bi-directional long short-term memory network. 
      The experimental results showed that the DeepHLAPred reached the state-of-the-art 
      performanceson the cross-validation test and the independent test. The extensive 
      test demonstrated the rationality of the DeepHLAPred. We further analyzed 
      sequence pattern of non-classical HLA class I binders by information entropy. The 
      information entropy of non-classical HLA binder sequence implied sequence pattern 
      to a certain extent. In addition, we have developed a user-friendly webserver for 
      convenient use, which is available at http://www.biolscience.cn/DeepHLApred/ . 
      The tool and the analysis is helpful to detect non-classical HLA Class I binder. 
      The source code and data is available at 
      https://github.com/tangxingyu0/DeepHLApred .
CI  - (c) 2023. The Author(s).
FAU - Huang, Guohua
AU  - Huang G
AD  - School of Information Technology and Administration, Hunan University of Finance 
      and Economics, Changsha, Hunan, 410215, China. guohuahhn@163.com.
AD  - College of Information Science and Engineering, Shaoyang University, Shaoyang, 
      Hunan, 422000, China. guohuahhn@163.com.
FAU - Tang, Xingyu
AU  - Tang X
AD  - College of Information Science and Engineering, Shaoyang University, Shaoyang, 
      Hunan, 422000, China.
FAU - Zheng, Peijie
AU  - Zheng P
AD  - College of Information Science and Engineering, Shaoyang University, Shaoyang, 
      Hunan, 422000, China.
LA  - eng
GR  - 62272310/National Natural Science Foundation of China/
GR  - 2022JJ50177/Hunan Province Natural Science Foundation of China/
PT  - Journal Article
DEP - 20231123
PL  - England
TA  - BMC Genomics
JT  - BMC genomics
JID - 100965258
RN  - 0 (Histocompatibility Antigens Class I)
RN  - 0 (HLA Antigens)
RN  - 0 (Histocompatibility Antigens Class II)
SB  - IM
MH  - Humans
MH  - *Deep Learning
MH  - Neural Networks, Computer
MH  - Histocompatibility Antigens Class I
MH  - HLA Antigens
MH  - Histocompatibility Antigens Class II
PMC - PMC10666343
OTO - NOTNLM
OT  - Convolutional neural network
OT  - Deep learning
OT  - Information entropy
OT  - Non-classical HLA class I
OT  - Representation
COIS- The authors declare no competing interests.
EDAT- 2023/11/23 00:42
MHDA- 2023/11/27 12:42
PMCR- 2023/11/23
CRDT- 2023/11/22 23:58
PHST- 2023/06/20 00:00 [received]
PHST- 2023/11/08 00:00 [accepted]
PHST- 2023/11/27 12:42 [medline]
PHST- 2023/11/23 00:42 [pubmed]
PHST- 2023/11/22 23:58 [entrez]
PHST- 2023/11/23 00:00 [pmc-release]
AID - 10.1186/s12864-023-09796-2 [pii]
AID - 9796 [pii]
AID - 10.1186/s12864-023-09796-2 [doi]
PST - epublish
SO  - BMC Genomics. 2023 Nov 23;24(1):706. doi: 10.1186/s12864-023-09796-2.