PMID- 31205413
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20231011
IS  - 1176-9351 (Print)
IS  - 1176-9351 (Electronic)
IS  - 1176-9351 (Linking)
VI  - 18
DP  - 2019
TI  - Machine Learning-Enhanced T Cell Neoepitope Discovery for Immunotherapy Design.
PG  - 1176935119852081
LID - 10.1177/1176935119852081 [doi]
LID - 1176935119852081
AB  - Immune responses mediated by T cells are aimed at specific peptides, designated T 
      cell epitopes, that are recognized when bound to human leukocyte antigen (HLA) 
      molecules. The HLA genes are remarkably polymorphic in the human population 
      allowing a broad and fine-tuned capacity to bind a wide array of peptide 
      sequences. Polymorphisms might generate neoepitopes by impacting the HLA-peptide 
      interaction and potentially alter the level and type of generated T cell 
      responses. Multiple algorithms and tools based on machine learning (ML) have been 
      implemented and are able to predict HLA-peptide binding affinity with 
      considerable accuracy. Challenges in this field include the availability of 
      adequate epitope datasets for training and benchmarking and the development of 
      fully integrated pipelines going from next-generation sequencing to neoepitope 
      prediction and quality analysis metrics. Effectively predicting neoepitopes from 
      in silico data is a demanding task that has been facilitated by ML and will be of 
      great value for the future of personalized immunotherapies against cancer and 
      other diseases.
FAU - Martins, Joana
AU  - Martins J
AD  - Life and Health Sciences Research Institute (ICVS), School of Medicine, 
      University of Minho, Braga, Portugal.
AD  - ICVS/3B PT Government Associate Laboratory, Braga/Guimaraes, Portugal.
FAU - Magalhaes, Carlos
AU  - Magalhaes C
AD  - Life and Health Sciences Research Institute (ICVS), School of Medicine, 
      University of Minho, Braga, Portugal.
AD  - ICVS/3B PT Government Associate Laboratory, Braga/Guimaraes, Portugal.
FAU - Rocha, Miguel
AU  - Rocha M
AD  - Centre of Biological Engineering, University of Minho, Braga, Portugal.
FAU - Osorio, Nuno S
AU  - Osorio NS
AUID- ORCID: 0000-0003-0949-5399
AD  - Life and Health Sciences Research Institute (ICVS), School of Medicine, 
      University of Minho, Braga, Portugal.
AD  - ICVS/3B PT Government Associate Laboratory, Braga/Guimaraes, Portugal.
LA  - eng
PT  - Journal Article
PT  - Review
DEP - 20190523
PL  - United States
TA  - Cancer Inform
JT  - Cancer informatics
JID - 101258149
PMC - PMC6535895
OTO - NOTNLM
OT  - T cells
OT  - epitope prediction
OT  - immunotherapy
OT  - machine learning
OT  - neoepitopes
COIS- declaration of conflicting interests:The author(s) declared no potential 
      conflicts of interest with respect to the research, authorship, and/or 
      publication of this article.
EDAT- 2019/06/18 06:00
MHDA- 2019/06/18 06:01
PMCR- 2019/05/23
CRDT- 2019/06/18 06:00
PHST- 2019/04/26 00:00 [received]
PHST- 2019/04/29 00:00 [accepted]
PHST- 2019/06/18 06:00 [entrez]
PHST- 2019/06/18 06:00 [pubmed]
PHST- 2019/06/18 06:01 [medline]
PHST- 2019/05/23 00:00 [pmc-release]
AID - 10.1177_1176935119852081 [pii]
AID - 10.1177/1176935119852081 [doi]
PST - epublish
SO  - Cancer Inform. 2019 May 23;18:1176935119852081. doi: 10.1177/1176935119852081. 
      eCollection 2019.