Xinxiang Li

ECC-MR improves Mendelian randomization by using error-correcting code principles to handle pleiotropy and linkage disequilibrium, providing more accurate causal estimates.

This study analyzes CNA signatures in single-cell and bulk samples of hepatocellular carcinoma, revealing masked patterns and potential biomarkers for prognosis and treatment.

This study uses deep learning to predict metabolic vulnerabilities in cancer cells and validates findings using TCGA data, showing improved clinical responses to targeted therapies.

The paper identifies FLAD1 as a key gene for hypoxia adaptation in tumors and develops a deep learning model to predict hypoxia-dependent metabolic genes.