PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 12, 2024Bioinformatics5 citationsOpen Access

NeoaPred: A deep-learning framework for predicting immunogenic neoantigen based on surface and structural features of peptide-HLA complexes

View Full Paper
DJDawei JiangBXBinbin XiWTW.C. Tan

Key Points

Key points are not available for this paper at this time.

Abstract

Neoantigens, derived from somatic mutations in cancer cells, can elicit anti-tumor immune responses when presented to autologous T cells by human leukocyte antigen (HLA). Identifying immunogenic neoantigens is crucial for cancer immunotherapy development. However, the accuracy of current bioinformatic methods remains unsatisfactory. Surface and structural features of peptide-HLA class I (pHLA-I) complexes offer valuable insight into the immunogenicity of neoantigens.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Jiang et al. (2024) studied this question.

synapsesocial.com/papers/68e58a69b6db643587526baehttps://doi.org/10.1093/bioinformatics/btae547
Ask AI
Helpful
Bookmark
Share
View Full Paper