Minjun Chen Source Confirmed

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

High Impact

Researcher

National Center for Toxicological Research

faculty

42 h-index 170 pubs 5,586 cited

Research Areas

Drug-Induced Hepatotoxicity and Protection Pharmacological Effects and Toxicity Studies Natural Language Processing Techniques Advanced Neural Network Applications Meta-analysis and systematic reviews Computational Drug Discovery Methods Liver Disease Diagnosis and Treatment

Links

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OverviewAI-generated summary

Minjun Chen's research focuses on the development and application of computational and in silico methods for predicting drug-induced liver injury (DILI) and other forms of hepatotoxicity. This work leverages machine learning, deep learning, and natural language processing techniques to analyze complex biological and textual data.

Chen has investigated the performance of preclinical models in predicting human drug responses, particularly concerning liver injury. Recent publications explore the use of BERT-based natural language processing for classifying DILI risk from drug labeling documents and the development of computational models for predicting liver toxicity within the context of deep learning. Furthermore, Chen's research includes the assessment of physiological liver microtissue systems for preclinical hepatotoxicity evaluation and the validation of machine learning models for predicting DILI using failed drug candidates.

Chen collaborates with researchers at the National Center for Toxicological Research, including Tsung‐Jen Liao, Kristin Ashby, Tucker A. Patterson, and Weida Tong, with whom Chen has co-authored multiple publications. Chen's scholarly contributions are reflected in an h-index of 42 and over 5,500 citations across more than 170 publications, designating Chen as a highly cited researcher.

Metrics

h-index: 42
Publications: 170
Citations: 5,586

Selected Publications

DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review (2025) DOI
Machine learning and artificial intelligence methods for predicting liver toxicity (2025) DOI
New approach methodologies (NAMs) for drug-induced liver injury (DILI): Where are we now? (2025) DOI
Artificial Intelligence: An Emerging Tool for Studying Drug‐Induced Liver Injury (2025) DOI
Genetic Variants of <i>GBP4</i>: Reduced Risks for Drug‐Induced Acute Liver Failure in Non‐Finnish European Population (2025) DOI
Physiological liver microtissue 384-well microplate system for preclinical hepatotoxicity assessment of therapeutic small molecule drugs (2024) DOI
Drug interaction with UDP-Glucuronosyltransferase (UGT) enzymes is a predictor of drug-induced liver injury (2024) DOI
Automatic text classification of drug-induced liver injury using document-term matrix and XGBoost (2024) DOI
Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development (2024) DOI
Composite Plot for Visualizing Aminotransferase and Bilirubin Changes in Clinical Trials of Subjects with Abnormal Baseline Values (2024) DOI
Machine Learning to Predict Drug-Induced Liver Injury and its Validation on Failed Drug Candidates in Development (2024) DOI
Medical device report analyses from MAUDE: Device and patient outcomes, adverse events, and sex-based differential effects (2024) DOI
Computational models for predicting liver toxicity in the deep learning era (2024) DOI
New Alternative Methods in Drug Safety Assessment (2023) DOI
QSAR modeling for predicting drug-induced liver injury (2023) DOI