Xiaoquan Su

This study develops Meta-iTL, an instance-based transfer learning method, to improve early CRC detection at the ADA stage by leveraging cross-cohort microbiome data.

This paper introduces a deep learning method that detects multiple diseases simultaneously using microbiome data and host variables, outperforming traditional ML approaches and capturing key disease-related factors.

The paper uses bi-clustering to identify gene clusters with consistent expression-protein abundance correlations and builds a prediction model to forecast protein abundance from mRNA data.