Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

An Analytic Pipeline to Obtain Reliable Genetic Ancestry Estimates from Tumor-Derived RNA Sequencing Data

Published in Cancer Epidemiology, Biomarkers & Prevention, 2025

A computational pipeline using tumor RNASeq data accurately estimates global genetic ancestry in the absence of germline DNA.

Recommended citation: Johnson C.E., **Ran X.**, Wrobel J., Davidson N.R., Greene C.S., Epstein M.P., Marks J.R., Peres L.C., Doherty J.A., & Schildkraut J.M. (2025). "An Analytic Pipeline to Obtain Reliable Genetic Ancestry Estimates from Tumor-Derived RNA Sequencing Data." Cancer Epidemiology, Biomarkers & Prevention. PMID:40622249
Download Paper

Gut Microbiota Modulation by Inulin Improves Metabolism and Ovarian Function in Polycystic Ovary Syndrome

Published in Advanced Science, 2025

Inulin reshapes the gut microbiota to enhance metabolic health and ovarian function in PCOS patients and mouse models.

Recommended citation: Geng L., Yang X., Sun J., Ran X., Zhou D., Ye M., Wen L., Wang R., & Chen M. (2025). “Gut Microbiota Modulation by Inulin Improves Metabolism and Ovarian Function in Polycystic Ovary Syndrome.” Advanced Science. doi:10.1002/advs.202412558
Download Paper

Bile acid–gut microbiota imbalance in cholestasis and its long-term effect in mice

Published in mSystems, 2024

This study demonstrates that temporary hepatic cholestasis in mice induces persistent gut microbiota dysbiosis, characterized by reduced diversity, overgrowth of pathobionts, and sustained inflammation post-recovery.

Recommended citation: Yang X., Xu Y., Li J., **Ran X.**, Gu Z., Song L., Zhang L., Wen L., Ji G., & Wang R. (2024). “Bile acid–gut microbiota imbalance in cholestasis and its long-term effect in mice.” mSystems, 9(7):e00127-24.
Download Paper

Conference Papers

Predict Opioid-using Pharmacists from the Prescribing Practices Based on SVM Classification Model

Published in Atlantis Press SARL (ASSEHR), 2022

A support vector machine model achieves 85% accuracy in predicting pharmacists’ propensity to prescribe opioids using CMS prescribing data.

Recommended citation: Ran, X. (2022). “Predict Opioid-using Pharmacists from the Prescribing Practices Based on SVM Classification Model.” In: Proceedings of the International Conference on Applied Science, Engineering and Healthcare Research (ASSEHR). Atlantis Press SARL. doi:10.2991/assehr.k.220110.050
Download Paper

Ximing Ran