Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy.

Journal: BMC bioinformatics
PMID: 33375939

Abstract

BACKGROUND: Personalized cancer vaccines are emerging as one of the most promising approaches to immunotherapy of advanced cancers. However, only a small proportion of the neoepitopes generated by somatic DNA mutations in cancer cells lead to tumor rejection. Since it is impractical to experimentally assess all candidate neoepitopes prior to vaccination, developing accurate methods for predicting tumor-rejection mediating neoepitopes (TRMNs) is critical for enabling routine clinical use of cancer vaccines.

Authors

  • Elham Sherafat
    Computer Science and Engineering Department, University of Connecticut, Storrs, CT, 06269, USA.
  • Jordan Force
    Computer Science and Engineering Department, University of Connecticut, Storrs, CT, 06269, USA.
  • Ion I Măndoiu
    Computer Science and Engineering Department, University of Connecticut, Storrs, CT, 06269, USA. ion@engr.uconn.edu.

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