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DeepAntigen: a novel method for neoantigen prioritization via 3D genome and deep sparse learning.

  • Shi, Yi1, 2, 3
  • Guo, Zehua2, 4
  • Su, Xianbin1
  • Meng, Luming5
  • Zhang, Mingxuan6
  • Sun, Jing7
  • Wu, Chao7
  • Zheng, Minhua7
  • Shang, Xueyin1
  • Zou, Xin1
  • Cheng, Wangqiu2, 3
  • Yu, Yaoliang8
  • Cai, Yujia1
  • Zhang, Chaoyi9
  • Cai, Weidong9
  • Da, Lin-Tai1
  • He, Guang2, 3
  • Han, Ze-Guang1
  • 1 Key Laboratory of Systems Biomedicine (Ministry of Education), Shanghai Centre for Systems Biomedicine, Shanghai Jiao Tong University, Shanghai 200240, China. , (China)
  • 2 Shanghai Jiao Tong University, Shanghai 200030, China. , (China)
  • 3 Shanghai Key Laboratory of Psychotic Disorders, and Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai 200030, China. , (China)
  • 4 Department of Instrument Science and Engineering, School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China. , (China)
  • 5 College of Biophotonics, South China Normal University, Guangzhou 510631, China. , (China)
  • 6 Department of Mathematics, University of California San Diego, La Jolla, CA 92093-0112, USA.
  • 7 Department of General Surgery & Shanghai Minimally Invasive Surgery Center, Ruijin Hospital, Shanghai Jiao Tong University, Shanghai 200025, China. , (China)
  • 8 David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, ON N2L3G1, Canada. , (Canada)
  • 9 School of Computer Science, The University of Sydney, Darlington, NSW, 2008, Australia. , (Australia)
Published Article
Bioinformatics (Oxford, England)
Publication Date
Dec 08, 2020
DOI: 10.1093/bioinformatics/btaa596
PMID: 32592462


The mutations of cancers can encode the seeds of their own destruction, in the form of T-cell recognizable immunogenic peptides, also known as neoantigens. It is computationally challenging, however, to accurately prioritize the potential neoantigen candidates according to their ability of activating the T-cell immunoresponse, especially when the somatic mutations are abundant. Although a few neoantigen prioritization methods have been proposed to address this issue, advanced machine learning model that is specifically designed to tackle this problem is still lacking. Moreover, none of the existing methods considers the original DNA loci of the neoantigens in the perspective of 3D genome which may provide key information for inferring neoantigens' immunogenicity. In this study, we discovered that DNA loci of the immunopositive and immunonegative MHC-I neoantigens have distinct spatial distribution patterns across the genome. We therefore used the 3D genome information along with an ensemble pMHC-I coding strategy, and developed a group feature selection-based deep sparse neural network model (DNN-GFS) that is optimized for neoantigen prioritization. DNN-GFS demonstrated increased neoantigen prioritization power comparing to existing sequence-based approaches. We also developed a webserver named deepAntigen ( that implements the DNN-GFS as well as other machine learning methods. We believe that this work provides a new perspective toward more accurate neoantigen prediction which eventually contribute to personalized cancer immunotherapy. Data and implementation are available on webserver: Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

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