S
Saniya Khullar
Researcher at University of Wisconsin-Madison
Publications - 15
Citations - 97
Saniya Khullar is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Biology & Medicine. The author has an hindex of 3, co-authored 6 publications receiving 25 citations.
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Journal ArticleDOI
The landscape of antibody binding in SARS-CoV-2 infection.
Anna S. Heffron,Sean J. McIlwain,Maya F. Amjadi,David A. Baker,Saniya Khullar,Tammy Armbrust,Peter Halfmann,Yoshihiro Kawaoka,Ajay K. Sethi,Ann C. Palmenberg,Miriam A. Shelef,David H. O’Connor,Irene M. Ong +12 more
TL;DR: In this article, the authors used ultradense peptide microarray mapping to show that SARS-CoV-2 infection induces robust antibody responses to epitopes throughout the SARS CoV2 proteome, particularly in M, in which 1 epitope achieved excellent diagnostic accuracy.
Posted ContentDOI
The landscape of antibody binding to SARS-CoV-2
Anna S. Heffron,Sean J. McIlwain,David A. Baker,Maya F. Amjadi,Saniya Khullar,Ajay K. Sethi,Miriam A. Shelef,David H. O’Connor,Irene M. Ong +8 more
TL;DR: It is shown that Sars-CoV-2 infection induces robust antibody responses to epitopes throughout the SARS-Cov-2 proteome, particularly in M, in which one epitope achieved near-perfect diagnostic accuracy.
Posted ContentDOI
The landscape of antibody binding in SARS-CoV-2 infection.
Anna S. Heffron,Sean J. McIlwain,Maya F. Amjadi,David A. Baker,Saniya Khullar,Ajay K. Sethi,Ann C. Palmenberg,Miriam A. Shelef,David H. O’Connor,Irene M. Ong +9 more
TL;DR: In this article, the authors used ultradense peptide microarray mapping to show that SARS-CoV-2 infection induces robust antibody responses to epitopes throughout the SARS CoV2 proteome, particularly in M, in which one epitope achieved excellent diagnostic accuracy.
Journal ArticleDOI
Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases
Chirag Gupta,Pramod Chandrashekar,Tingyu Jin,Chenfeng He,Saniya Khullar,Qiang Chang,Daifeng Wang +6 more
TL;DR: In this article , a review of the application of ML technologies to Intellectual and Developmental Disabilities (IDDs) can be found, which can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development.
Journal ArticleDOI
Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases
Chirag Gupta,Pramod Chandrashekar,Tingyu Jin,Chenfeng He,Saniya Khullar,Qiang Chang,Daifeng Wang +6 more
TL;DR: In this article , a review of the application of ML technologies to Intellectual and Developmental Disabilities (IDDs) can be found, which can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development.