Perfecting antigen prediction

Advances in genomics and precision measurement have continued to demonstrate the importance of the immune system across many disease types. At the heart of many emerging approaches to leverage these insights for precision immunotherapies is the computational antigen prediction problem. We propose a...

Full description

Saved in:
Bibliographic Details
Published in:The Journal of experimental medicine Vol. 219; no. 9
Main Authors: Hoyos, David, Greenbaum, Benjamin D
Format: Journal Article
Language:English
Published: United States 05.09.2022
Subjects:
Antigens
Genomics
Immunotherapy
ISSN:1540-9538, 1540-9538
Online Access:Get more information
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Advances in genomics and precision measurement have continued to demonstrate the importance of the immune system across many disease types. At the heart of many emerging approaches to leverage these insights for precision immunotherapies is the computational antigen prediction problem. We propose a threefold approach to improving antigen predictions: further defining the geometry of the antigen landscape, incorporating the coupling of antigen recognition to other cellular processes, and diversifying the training sets used for models that predict immunogenicity.
AbstractList Advances in genomics and precision measurement have continued to demonstrate the importance of the immune system across many disease types. At the heart of many emerging approaches to leverage these insights for precision immunotherapies is the computational antigen prediction problem. We propose a threefold approach to improving antigen predictions: further defining the geometry of the antigen landscape, incorporating the coupling of antigen recognition to other cellular processes, and diversifying the training sets used for models that predict immunogenicity.Advances in genomics and precision measurement have continued to demonstrate the importance of the immune system across many disease types. At the heart of many emerging approaches to leverage these insights for precision immunotherapies is the computational antigen prediction problem. We propose a threefold approach to improving antigen predictions: further defining the geometry of the antigen landscape, incorporating the coupling of antigen recognition to other cellular processes, and diversifying the training sets used for models that predict immunogenicity.
Advances in genomics and precision measurement have continued to demonstrate the importance of the immune system across many disease types. At the heart of many emerging approaches to leverage these insights for precision immunotherapies is the computational antigen prediction problem. We propose a threefold approach to improving antigen predictions: further defining the geometry of the antigen landscape, incorporating the coupling of antigen recognition to other cellular processes, and diversifying the training sets used for models that predict immunogenicity.
Author Hoyos, David
Greenbaum, Benjamin D
Author_xml – sequence: 1
  givenname: David
  surname: Hoyos
  fullname: Hoyos, David
  organization: Computational Oncology, Department of Epidemiology & Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
– sequence: 2
  givenname: Benjamin D
  orcidid: 0000-0001-6153-8793
  surname: Greenbaum
  fullname: Greenbaum, Benjamin D
  organization: Physiology, Biophysics & Systems Biology, Weill Cornell Medicine, Weill Cornell Medical College, New York, NY
BackLink https://www.ncbi.nlm.nih.gov/pubmed/35972475$$D View this record in MEDLINE/PubMed
BookMark eNpNj81LxDAUxIOsuB9686rs0UvX5OWlSY-yrB-woAc9lzR9Wbq0aW3ag_-9FVfwNMPwY4ZZslloAzF2LfhGcIP3R2o2wAEmn56xhVDIk0xJM_vn52wZ45FzgajSCzaXKtOAWi3YzRv1ntxQhcPahqE6UFh3PZXVFLXhkp17W0e6OumKfTzu3rfPyf716WX7sE-cNDgkrnSlBks-LUEpVAjWcZNxnWZWFmS09uimTdTkjAPvnbBeCul1oZwggBW7--3t-vZzpDjkTRUd1bUN1I4xB80h0ymaH_T2hI5FQ2Xe9VVj-6_87xJ8A9U0TgI
CitedBy_id crossref_primary_10_1016_j_intimp_2024_112811
crossref_primary_10_1021_acsinfecdis_4c01053
crossref_primary_10_3389_fimmu_2023_1238586
ContentType Journal Article
Copyright 2022 Hoyos and Greenbaum.
Copyright_xml – notice: 2022 Hoyos and Greenbaum.
DBID CGR
CUY
CVF
ECM
EIF
NPM
7X8
DOI 10.1084/jem.20220846
DatabaseName Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
MEDLINE - Academic
DatabaseTitle MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
MEDLINE - Academic
DatabaseTitleList MEDLINE - Academic
MEDLINE
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: 7X8
  name: MEDLINE - Academic
  url: https://search.proquest.com/medline
  sourceTypes: Aggregation Database
DeliveryMethod no_fulltext_linktorsrc
Discipline Medicine
EISSN 1540-9538
ExternalDocumentID 35972475
Genre Journal Article
GroupedDBID ---
-~X
18M
29K
2WC
36B
4.4
53G
5GY
5RE
5VS
ABOCM
ABZEH
ACGFO
ACNCT
ACPRK
ADBBV
AENEX
AFOSN
AFRAH
ALMA_UNASSIGNED_HOLDINGS
AOIJS
BAWUL
BTFSW
C45
CGR
CS3
CUY
CVF
D-I
DIK
DU5
E3Z
EBS
ECM
EIF
EMB
F5P
F9R
GX1
H13
HYE
IH2
KQ8
L7B
N9A
NPM
O5R
O5S
OK1
P2P
P6G
R.V
RHI
SJN
TR2
TRP
UHB
W8F
WOQ
7X8
ID FETCH-LOGICAL-c384t-cdcd72aef6d2554542ac0890769a3be877f4c35947ec8c2ffc1af313f7b5c1e22
IEDL.DBID 7X8
ISICitedReferencesCount 3
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000861562400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 1540-9538
IngestDate Fri Sep 05 13:13:16 EDT 2025
Thu Apr 03 07:02:35 EDT 2025
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 9
Language English
License 2022 Hoyos and Greenbaum.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c384t-cdcd72aef6d2554542ac0890769a3be877f4c35947ec8c2ffc1af313f7b5c1e22
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ORCID 0000-0001-6153-8793
OpenAccessLink https://pubmed.ncbi.nlm.nih.gov/PMC9386507
PMID 35972475
PQID 2702976482
PQPubID 23479
ParticipantIDs proquest_miscellaneous_2702976482
pubmed_primary_35972475
PublicationCentury 2000
PublicationDate 2022-09-05
PublicationDateYYYYMMDD 2022-09-05
PublicationDate_xml – month: 09
  year: 2022
  text: 2022-09-05
  day: 05
PublicationDecade 2020
PublicationPlace United States
PublicationPlace_xml – name: United States
PublicationTitle The Journal of experimental medicine
PublicationTitleAlternate J Exp Med
PublicationYear 2022
SSID ssj0014456
Score 2.4257107
Snippet Advances in genomics and precision measurement have continued to demonstrate the importance of the immune system across many disease types. At the heart of...
SourceID proquest
pubmed
SourceType Aggregation Database
Index Database
SubjectTerms Antigens
Genomics
Immunotherapy
Title Perfecting antigen prediction
URI https://www.ncbi.nlm.nih.gov/pubmed/35972475
https://www.proquest.com/docview/2702976482
Volume 219
WOSCitedRecordID wos000861562400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LSwMxEB7Uinjx_ahaWcFraDebbLInEbF4aelBobclm0xED9un_n4n25SeBMHLElhCknkkk3yTfAD3LvOKon7FnJYFE6aoWIUpMl_llvZixhonGrIJNRzq8bgYxQO3RUyrXM-JzUTtJjackXfDvSlaOoXmD9MZC6xRAV2NFBrb0MoolAlWrcYbFEGIhr01DeB_QZ4dE997WnQ_MVxD55zK-e_BZbPI9A__270jOIjhZfK4sodj2ML6BPYGEUA_hc4I5032Vf2ekEzDU5zJdB5-BwWdwVv_-fXphUWGBGYzLZbMOusUN-hzR1sDIQU3tqdJxnlhsgq1Ul7YTBZCodWWe29T40lSXlXSpsj5OezUkxovISHPNYWTGjWiSDnVFSgDypaZQPnXa8PdeuAlWWCAFUyNk69FuRl6Gy5W0iunq6cySmpbcaHk1R9qX8N-0EiTwCVvoOXJ_7ADu_Z7-bGY3zaqpe9wNPgBnpCtIg
linkProvider ProQuest
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Perfecting+antigen+prediction&rft.jtitle=The+Journal+of+experimental+medicine&rft.au=Hoyos%2C+David&rft.au=Greenbaum%2C+Benjamin+D&rft.date=2022-09-05&rft.eissn=1540-9538&rft.volume=219&rft.issue=9&rft_id=info:doi/10.1084%2Fjem.20220846&rft_id=info%3Apmid%2F35972475&rft_id=info%3Apmid%2F35972475&rft.externalDocID=35972475
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1540-9538&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1540-9538&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1540-9538&client=summon