sNebula, a network-based algorithm to predict binding between human leukocyte antigens and peptides [electronic resource]

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 611.3 Digestive tract organs

Thông tin xuất bản: Oak Ridge, Tenn. : Oak Ridge, Tenn. : Oak Ridge Institute for Science and Education ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2016

Mô tả vật lý: Size: Article No. 32115 : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 260743

Understanding the binding between human leukocyte antigens (HLAs) and peptides is important to understand the functioning of the immune system. Since it is time-consuming and costly to measure the binding between large numbers of HLAs and peptides, computational methods including machine learning models and network approaches have been developed to predict HLA-peptide binding. However, there are several limitations for the existing methods. We developed a network-based algorithm called sNebula to address these limitations. We curated qualitative Class I HLA-peptide binding data and demonstrated the prediction performance of sNebula on this dataset using leave-one-out cross-validation and five-fold cross-validations. Furthermore, this algorithm can predict not only peptides of different lengths and different types of HLAs, but also the peptides or HLAs that have no existing binding data. We believe sNebula is an effective method to predict HLA-peptide binding and thus improve our understanding of the immune system.
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