Ensemble deep learning in bioinformatics
The remarkable flexibility and adaptability of ensemble methods and deep learning models
have led to the proliferation of their application in bioinformatics research. Traditionally …
have led to the proliferation of their application in bioinformatics research. Traditionally …
Technological advances in cancer immunity: from immunogenomics to single-cell analysis and artificial intelligence
Y Xu, GH Su, D Ma, Y Xiao, ZM Shao… - Signal Transduction and …, 2021 - nature.com
Immunotherapies play critical roles in cancer treatment. However, given that only a few
patients respond to immune checkpoint blockades and other immunotherapeutic strategies …
patients respond to immune checkpoint blockades and other immunotherapeutic strategies …
A transformer-based model to predict peptide–HLA class I binding and optimize mutated peptides for vaccine design
Human leukocyte antigen (HLA) can recognize and bind foreign peptides to present them to
specialized immune cells, then initiate an immune response. Computational prediction of the …
specialized immune cells, then initiate an immune response. Computational prediction of the …
Recent progress in the discovery and design of antimicrobial peptides using traditional machine learning and deep learning
Antimicrobial resistance has become a critical global health problem due to the abuse of
conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides …
conventional antibiotics and the rise of multi-drug-resistant microbes. Antimicrobial peptides …
Prediction of specific TCR-peptide binding from large dictionaries of TCR-peptide pairs
I Springer, H Besser, N Tickotsky-Moskovitz… - Frontiers in …, 2020 - frontiersin.org
Current sequencing methods allow for detailed samples of T cell receptors (TCR)
repertoires. To determine from a repertoire whether its host had been exposed to a target …
repertoires. To determine from a repertoire whether its host had been exposed to a target …
Current challenges for unseen-epitope TCR interaction prediction and a new perspective derived from image classification
The prediction of epitope recognition by T-cell receptors (TCRs) has seen many
advancements in recent years, with several methods now available that can predict …
advancements in recent years, with several methods now available that can predict …
DeepImmuno: deep learning-empowered prediction and generation of immunogenic peptides for T-cell immunity
Cytolytic T-cells play an essential role in the adaptive immune system by seeking out,
binding and killing cells that present foreign antigens on their surface. An improved …
binding and killing cells that present foreign antigens on their surface. An improved …
Cancer neoantigens: challenges and future directions for prediction, prioritization, and validation
Prioritization of immunogenic neoantigens is key to enhancing cancer immunotherapy
through the development of personalized vaccines, adoptive T cell therapy, and the …
through the development of personalized vaccines, adoptive T cell therapy, and the …
DeepHLApan: a deep learning approach for neoantigen prediction considering both HLA-peptide binding and immunogenicity
Neoantigens play important roles in cancer immunotherapy. Current methods used for
neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and …
neoantigen prediction focus on the binding between human leukocyte antigens (HLAs) and …