Zhixin Cyrillus Tan

This study integrates time-series cytokine and liver function data from liver transplant patients to identify immunological signatures linked to disease etiology, injury, and CD4+ T cell polarization affecting allograft survival.

The paper introduces scSLIDE, a framework that generates sample-level embeddings from single-cell data to analyze variation across samples in disease and development.

This study compares censored least squares to other methods for imputing missing values in tensor factorization of biological datasets, finding censored least squares to be more accurate and efficient.

This paper introduces a multivalent binding model to infer antibody structural features from systems serology data, revealing insights into vaccine efficacy and disease progression.

This paper introduces a general model for predicting multivalent ligand binding to cells with heterogeneous receptors and multiple surface ligands, enabling large-scale binding predictions.