Skip to main content
Harrison Zhang on Muck Rack

Harrison Zhang

As seen in: Nature, Cell Press, Cronkite News, The State Press, Today’s News-Herald, The Verde Independent, Signals
Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work. Claim your profile

Get in touch with Harrison

Contact Harrison, search articles and posts on X, monitor coverage, and track replies from one place.

Learn more about Muck Rack

Actions

Share this page

Is this you?

As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile

Articles

Phenotypic prediction of missense variants via deep contrastive learning

|
By Jun Wen, Sihang Zeng, Clara-Lea Bonzel, Shilpa Nadimpalli Kobren, Jiangchuan Du, Yi Chai, Hao Wang, Meng Zhu, Siwei Chen, Fangwei Leng, Harrison Zhang, Katherine P Liao, Kelly Kasulis Cho, Isaac S. Kohane, Marinka Zitnik, Alexandre C. Pereira, Jun Liu, Tianxi Cai
| Nature Verified
Abstract Missense variants (MVs) influence clinical phenotypes, but our understanding of their phenotypic consequences remains constrained. Existing computational approaches to interpret MVs predominantly assess their pathogenicity, without considering phenotypic heterogeneity.
Open in Who Shared Issue with article?

Label efficient phenotyping for Long COVID using electronic health records - npj Digital Medicine

|
By Jun Wen, Harrison Zhang, Vidul Ayakulangara Panickan, Doris Yang, Xin Xiong, Yuk-Lam Ho, Shyam Visweswaran, Gabriel A. Brat
| Nature Verified
Abstract Long COVID poses a significant disease burden globally, but its heterogeneous presentation and unreliable coding practices render it difficult to study. Developing efficient phenotyping algorithms is crucial to enabling risk prediction and effective management of Long COVID. We introduce the LAbel-efficienT Long COVID pHenotyping (LATCH) algorithm, which synthesizes a small number of gold-standard labels and a large, unlabeled dataset with many electronic health record (EHR) features.
Open in Who Shared Issue with article?

Potential pitfalls in the use of real-world data for studying long COVID - Nature Medicine

|
By Harrison Zhang, Jacqueline P. Honerlaw, Sehi L’Yi, Yuk-Lam Ho, Vidul Ayakulangara Panickan, Katherine P Liao, Shyam Visweswaran, Isaac S. Kohane, Zongqi Xia, Tianxi Cai
| Nature Verified
The value of large-scale, real-world data — from electronic health records (EHRs), for example — has been used to establish vaccine efficacy, to elucidate the genetic etiologies of diseases, and to advance epidemiological research1,2,3. Real-world data also have the potential to capture the wide spectrum of clinical features attributed to post-acute sequalae of SARS-CoV-2, also called long COVID, in diverse patient populations4.
Open in Who Shared Issue with article?
See all 28 articles