Wen Zou Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

High Impact

Researcher

National Center for Toxicological Research

faculty

25 h-index 104 pubs 2,850 cited

Research Areas

Machine Learning Models Pharmacological Effects and Toxicity Studies Natural Language Processing Techniques Health and Medical Research Impacts Meta-analysis and systematic reviews Advanced Neural Network Applications Carcinogens and Genotoxicity Assessment

Links

ORCID Lab Website Research Group

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OverviewAI-generated summary

Wen Zou's research focuses on the application of machine learning and data mining techniques to analyze complex biological and health-related datasets. This work includes developing and evaluating computational models for predicting toxicity, such as liver toxicity, and normalizing drug names for use in adverse event reporting systems. Zou has investigated opioid-related adverse events submitted to the FDA Adverse Event Reporting System (FAERS), employing methods like Latent Dirichlet Allocation (LDA) and BERTopic for topic modeling to understand cardiovascular risks associated with opioid use in women.

Further research extends to pattern recognition in environmental contaminant data, specifically identifying persistent organic pollutants in food sources. Zou has also contributed to the field of pharmacovigilance and pharmacoepidemiology through editorial work on AI/ML applications. The researcher maintains an active lab website and leads a research group, collaborating with colleagues such as Weigong Ge, Joe Meehan, Bohu Pan, and Beverly Lyn-Cook, all from the National Center for Toxicological Research, with whom Zou has co-authored numerous publications.

With 104 total publications and 2,850 citations, Zou has achieved an h-index of 25, reflecting a significant body of scholarly work. This productivity and impact have led to the designation of Zou as a high-impact researcher.

Metrics

h-index: 25
Publications: 104
Citations: 2,850

Selected Publications

AI-powered topic modeling: comparing LDA and BERTopic in analyzing opioid-related cardiovascular risks in women (2025) DOI
Editorial: AI/ML in pharmacovigilance and pharmacoepidemiology (2024) DOI
A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database (2023) DOI
Decision forest—a machine learning algorithm for QSAR modeling (2023) DOI
Additional file 5 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022) DOI
Additional file 15 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022) DOI
Additional file 10 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022) DOI
Additional file 9 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022) DOI
Additional file 11 of Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022) DOI
Machine Learning Models for Predicting Liver Toxicity (2022) DOI
Assessing reproducibility of inherited variants detected with short-read whole genome sequencing (2022) DOI
Text Fingerprinting and Topic Mining in the Prescription Opioid Use Literature (2021) DOI
Discovering Drug-Drug Associations in the FDA Adverse Event Reporting System Database with Data Mining Approaches (2021) DOI
Text Fingerprinting and Topic Mining in the Prescription Opioid Use Literature (2021) DOI
Software-Assisted Pattern Recognition of Persistent Organic Pollutants in Contaminated Human and Animal Food (2021) DOI