Zheng Quan Xu

This paper introduces EBHiC, an empirical Bayes method for peak detection in Hi-C data, showing improved accuracy and consistency compared to Fit-Hi-C.

This paper evaluates machine learning algorithms for predicting gene functions without relying on homology, finding random forest provides the most accurate results, particularly in Biological Process GO terms.

The paper develops an improved Hi-C protocol and bias-correction pipeline to map chromatin loops in human neurogenesis and brain tissues, showing their role in neural differentiation and GWAS gene identification.

HUGIn is a web browser for visualizing Hi-C data from 21 human primary tissues and cell lines to assess chromatin contacts across and specific to tissues or cell lines.