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Artificial intelligence methods for predicting T-cell epitopes.

Research paper by Yingdong Y Zhao, Myong-Hee MH Sung, Richard R Simon

Indexed on: 03 May '08Published on: 03 May '08Published in: Methods in molecular biology (Clifton, N.J.)



Abstract

Identifying epitopes that elicit a major histocompatibility complex (MHC)-restricted T-cell response is critical for designing vaccines for infectious diseases and cancers. We have applied two artificial intelligence approaches to build models for predicting T-cell epitopes. We developed a support vector machine to predict T-cell epitopes for an MHC class I-restricted T-cell clone (TCC) using synthesized peptide data. For predicting T-cell epitopes for an MHC class II-restricted TCC, we built a shift model that integrated MHC-binding data and data from T-cell proliferation assay against a combinatorial library of peptide mixtures.