AIMC Topic: Epitopes
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HLAIImaster: a deep learning method with adaptive domain knowledge predicts HLA II neoepitope immunogenic responses.
Briefings in bioinformatics
May 23, 2024
While significant strides have been made in predicting neoepitopes that trigger autologous CD4+ T cell responses, accurately identifying the antigen presentation by human leukocyte antigen (HLA) class II molecules remains a challenge. This identifica...
PiTE: TCR-epitope Binding Affinity Prediction Pipeline using Transformer-based Sequence Encoder.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Jan 1, 2023
Accurate prediction of TCR binding affinity to a target antigen is important for development of immunotherapy strategies. Recent computational methods were built on various deep neural networks and used the evolutionary-based distance matrix BLOSUM t...
In silico proof of principle of machine learning-based antibody design at unconstrained scale.
mAbs
Jan 1, 2022
Generative machine learning (ML) has been postulated to become a major driver in the computational design of antigen-specific monoclonal antibodies (mAb). However, efforts to confirm this hypothesis have been hindered by the infeasibility of testing ...
Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.
Briefings in bioinformatics
Nov 5, 2021
Major histocompatibility complex (MHC) possesses important research value in the treatment of complex human diseases. A plethora of computational tools has been developed to predict MHC class I binders. Here, we comprehensively reviewed 27 up-to-date...
Vaxign2: the second generation of the first Web-based vaccine design program using reverse vaccinology and machine learning.
Nucleic acids research
Jul 2, 2021
Vaccination is one of the most significant inventions in medicine. Reverse vaccinology (RV) is a state-of-the-art technique to predict vaccine candidates from pathogen's genome(s). To promote vaccine development, we updated Vaxign2, the first web-bas...
Deep generative selection models of T and B cell receptor repertoires with soNNia.
Proceedings of the National Academy of Sciences of the United States of America
Apr 6, 2021
Subclasses of lymphocytes carry different functional roles to work together and produce an immune response and lasting immunity. Additionally to these functional roles, T and B cell lymphocytes rely on the diversity of their receptor chains to recogn...
Time series computational prediction of vaccines for influenza A H3N2 with recurrent neural networks.
Journal of bioinformatics and computational biology
Feb 1, 2020
Influenza viruses are persistently threatening public health, causing annual epidemics and sporadic pandemics due to rapid viral evolution. Vaccines are used to prevent influenza infections but the composition of the influenza vaccines have to be upd...
Complex Network Study of the Immune Epitope Database for Parasitic Organisms.
Current topics in medicinal chemistry
Feb 9, 2018
BACKGROUND: Complex network approach allows the representation and analysis of complex systems of interacting agents in an ordered and effective manner, thus increasing the probability of discovering significant properties of them. In the present stu...
Identification of errors in the IEDB using ontologies.
Database : the journal of biological databases and curation
Jan 1, 2018
The Immune Epitope Database (IEDB) is a free online resource that has manually curated over 18 500 references from the scientific literature. Our database presents experimental data relating to the recognition of immune epitopes by the adaptive immun...
FAIR principles and the IEDB: short-term improvements and a long-term vision of OBO-foundry mediated machine-actionable interoperability.
Database : the journal of biological databases and curation
Jan 1, 2018
The Immune Epitope Database (IEDB), at www.iedb.org, has the mission to make published experimental data relating to the recognition of immune epitopes easily available to the scientific public. By presenting curated data in a searchable database, we...
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