Zhaoqian Liu

An explainable graph neural framework to identify cancer-associated intratumoral microbial communities

This paper introduces MICAH, a graph neural network framework that identifies cancer-associated microbial communities in intratumoral microbiome data across five cancer types.

A Weighted Two-stage Sequence Alignment Framework to Identify DNA Motifs from ChIP-exo Data

TESA improves TFBS identification in ChIP-exo data by integrating nucleotide-resolution signal with a two-stage alignment framework, showing higher accuracy in E. coli datasets.

Inference of disease-associated microbial gene modules based on metagenomic and metatranscriptomic data

This paper introduces IDAM, a method for inferring disease-associated microbial gene modules from metagenomic and metatranscriptomic data, showing superior performance and reproducibility in inflammatory bowel disease datasets.

A Statistical Framework for Data Purification with Application to Microbiome Data Analysis

The paper introduces a data purification algorithm to improve detection of disease-associated microbes in microbiome data by reducing noise and heterogeneity.