Skip to main content
Zhixin Tan on Muck Rack

Zhixin Tan

As seen in: Nikkei Asia, BioRxiv
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 Zhixin

Contact Zhixin, 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

Tensor-Based Integration of Time-Series Measurements Reveals Relationships Between Underlying Disease, Early Injury, and CD4+ Polarization in Liver Transplantation

|
By Jackson Chin, Zhixin Tan, Rebecca Sosa, Ying Zheng
| BioRxiv
Abstract For patients with end-stage liver failure, liver transplantation (LT) remains the standard-of-care, though five-year allograft survival rates remain below 80%. Liver ischemia reperfusion injuries (LIRI) arise during transplant and contribute to allograft dysfunction.
Open in Who Shared Issue with article?

Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization

|
By Ethan Hung, Zhixin Tan, Aaron Meyer
| BioRxiv
Abstract Tensor factorization is a dimensionality reduction method applied to multidimensional arrays. These methods are useful for identifying patterns within a variety of biomedical datasets due to their ability to preserve the organizational structure of experiments and therefore aid in generating meaningful insights. However, missing data in the datasets being analyzed can impose challenges. Tensor factorization can be performed with some level of missing data and reconstruct a complete tensor.
Open in Who Shared Issue with article?

Multivalent binding model quantifies antibody species from systems serology

|
By Zhixin Tan, Priyanka Shrestha Verified, Armaan A Abraham, Emily R Bozich
| BioRxiv
Abstract Systems serology aims to broadly profile the antigen binding, Fc biophysical features, immune receptor engagement, and effector functions of antibodies. This experimental approach excels at identifying antibody functional features that are relevant to a particular disease. However, a crucial limitation of this approach is its incomplete description of what structural features of the antibodies are responsible for the observed immune receptor engagement and effector functions.
Open in Who Shared Issue with article?
See all 7 licensed articles (9 Total)