HLA class I binding prediction via convolutional neural networks | Zendy

Yeeleng S. Vang | Zendy; Xiaohui Xie | Zendy

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HLA class I binding prediction via convolutional neural networks

Author(s) -

Yeeleng S. Vang,

Xiaohui Xie

Publication year - 2017

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btx264

Subject(s) - human leukocyte antigen , computer science , convolutional neural network , major histocompatibility complex , benchmark (surveying) , computational biology , artificial intelligence , mhc class i , machine learning , biology , immune system , immunology , antigen , geodesy , geography

Many biological processes are governed by protein-ligand interactions. One such example is the recognition of self and non-self cells by the immune system. This immune response process is regulated by the major histocompatibility complex (MHC) protein which is encoded by the human leukocyte antigen (HLA) complex. Understanding the binding potential between MHC and peptides can lead to the design of more potent, peptide-based vaccines and immunotherapies for infectious autoimmune diseases.

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