Match tier
Likely match
Presence
Current · Arkansas
Last published
2026
Sources
OpenAlex · ORCID
Refreshed
2026-10-05
L
Leihong Wu
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
High Impact
Staff Fellow
Also affiliated: Qingdao University (2020); United States Food and Drug Administration (2015–2026); Center for Devices and Radiological Health (2021); Government of the United States of America (2021); Zhejiang University (2010–2014)
26 h-index
111 pubs
2,560 cited
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Leihong Wu's research focuses on the application of artificial intelligence and bioinformatics to regulatory science, particularly in the field of predictive toxicology and drug safety. Wu investigates methods for analyzing large-scale biological data, including genomics and sequencing information, to develop predictive models for toxicological outcomes and adverse drug events.
Wu has published on the analytical validity of sequencing assays for cancer mutation detection and the establishment of reference samples for benchmarking these techniques. Further research includes the development of classification models for drug-induced liver injury and the use of FDA-approved drug labeling and MedDRA to study serious adverse drug reactions. Wu also explores the trade-off between predictivity and explainability in machine learning models for toxicology using Tox21 datasets. Wu leads a research group and collaborates with researchers at the National Center for Toxicological Research and the University of Arkansas for Medical Sciences. Wu's work is supported by a strong publication record and citation metrics, including an h-index of 26 and over 2,500 citations.
Metrics
- h-index: 26
- Publications: 111
- Citations: 2,560
Positions
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Staff Fellow 2017–presentNational Center for Toxicological Research Division of Bioinformatics and Biostatistics ORCID
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Post Doc 2014–2017National Center for Toxicological Research Division of bioinformatics and biostatistics ORCID
Selected Publications
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Does generative AI mean the “end of history” for pharmacovigilance automation? towards a framework for the future of human-AI systems (2026)
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GANomics: bridging legacy and modern transcriptomic platforms for clinical applications (2026)
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Comparative Study of Molecular Descriptors and AI-Based Embeddings for Toxicity Prediction (2025)
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Benchmarking bias in embeddings of healthcare AI models: using SD-WEAT for detection and measurement across sensitive populations (2025)
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Assessing the developmental effects of fentanyl and impacts on lipidomic profiling using neural stem cell models (2025)
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Biomarkers of Neurotoxicity and Disease (2025)
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Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel (2025)
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Is ChatGPT Ready for Public Use in Organ-Specific Drug Toxicity Research? (2025)
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Enhancing pharmacogenomic data accessibility and drug safety with large language models: a case study with Llama3.1 (2024)
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Enhancing Bias Assessment for Complex Term Groups in Language Embedding Models: Quantitative Comparison of Methods (2024)
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Description and Validation of a Novel AI Tool, LabelComp, for the Identification of Adverse Event Changes in FDA Labeling (2024)
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Assessing the performance of large language models in literature screening for pharmacovigilance: a comparative study (2024)
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SD-WEAT: Towards Robustly Measuring Bias in Input Embeddings (2024)
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Text summarization with ChatGPT for drug labeling documents (2024)
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PERform: assessing model performance with predictivity and explainability readiness formula (2024)
Collaboration Network
Top Collaborators
Joshua Xu
National Center for Toxicological Research
40 shared publications
In database
- Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles
- Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA
- Trade-off Predictivity and Explainability for Machine-Learning Powered Predictive Toxicology: An in-Depth Investigation with Tox21 Data Sets
- Comprehensive Assessments of RNA-seq by the SEQC Consortium: FDA-Led Efforts Advance Precision Medicine
- Study of pharmacogenomic information in FDA-approved drug labeling to facilitate application of precision medicine
Showing 5 of 40 shared publications
Weida Tong
National Center for Toxicological Research
33 shared publications
In database
- Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity
- Comparing SVM and ANN based Machine Learning Methods for Species Identification of Food Contaminating Beetles
- Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA
- Comprehensive Assessments of RNA-seq by the SEQC Consortium: FDA-Led Efforts Advance Precision Medicine
- Study of pharmacogenomic information in FDA-approved drug labeling to facilitate application of precision medicine
Showing 5 of 33 shared publications
Hong Fang
National Center for Toxicological Research
9 shared publications
In database
- Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA
- Multiple microRNAs function as self-protective modules in acetaminophen-induced hepatotoxicity in humans
- Study of pharmacogenomic information in FDA-approved drug labeling to facilitate application of precision medicine
- Integrating Drug’s Mode of Action into Quantitative Structure–Activity Relationships for Improved Prediction of Drug-Induced Liver Injury
- Text summarization with ChatGPT for drug labeling documents
Showing 5 of 9 shared publications
Xiangwen Liu
University of Arkansas at Little Rock (US)
9 shared publications
- DLI-IT: a deep learning approach to drug label identification through image and text embedding
- HetEnc: a deep learning predictive model for multi-type biological dataset
- HetEnc: A Deep Learning Predictive Model for Multi-type Biological Dataset
- DLI-IT: A Deep Learning Approach to Drug Label Identification Through Image and Text Embedding
- HetEnc: A Deep Learning Predictive Model for Multi-type Biological Dataset
Showing 5 of 9 shared publications
Huixiao Hong
National Center for Toxicological Research
8 shared publications
In database
- Multiple microRNAs function as self-protective modules in acetaminophen-induced hepatotoxicity in humans
- Comprehensive Assessments of RNA-seq by the SEQC Consortium: FDA-Led Efforts Advance Precision Medicine
- Direct comparison of performance of single nucleotide variant calling in human genome with alignment-based and assembly-based approaches
- Challenges, Solutions, and Quality Metrics of Personal Genome Assembly in Advancing Precision Medicine
- Competitive docking model for prediction of the human nicotinic acetylcholine receptor α7 binding of tobacco constituents
Showing 5 of 8 shared publications
Magnus Gray
National Center for Toxicological Research
8 shared publications
In database
- Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science
- RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
- Classifying Free Texts Into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents
- Benchmarking bias in embeddings of healthcare AI models: using SD-WEAT for detection and measurement across sensitive populations
Showing 5 of 8 shared publications
James C. Willey
University of Toledo (US)
7 shared publications
- Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology
- Technical advance in targeted NGS analysis enables identification of lung cancer risk-associated low frequency TP53, PIK3CA, and BRAF mutations in airway epithelial cells
- Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA
- Abstract 1623: Inter-laboratory harmonization of next generation sequencing somatic mutation assays for cancer response prediction
- Abstract 432: Novel method for NGS analysis of actionable mutations in circulating tumor DNA specimens: improved quality control and 20-fold lower sequencing required
Showing 5 of 7 shared publications
Thomas Blomquist
University of Toledo (US)
7 shared publications
- Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing
- Technical advance in targeted NGS analysis enables identification of lung cancer risk-associated low frequency TP53, PIK3CA, and BRAF mutations in airway epithelial cells
- Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA
- Abstract 1623: Inter-laboratory harmonization of next generation sequencing somatic mutation assays for cancer response prediction
- Abstract 432: Novel method for NGS analysis of actionable mutations in circulating tumor DNA specimens: improved quality control and 20-fold lower sequencing required
Showing 5 of 7 shared publications
Wenming Xiao
United States Food and Drug Administration (US)
6 shared publications
- Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing
- Multiple microRNAs function as self-protective modules in acetaminophen-induced hepatotoxicity in humans
- Direct comparison of performance of single nucleotide variant calling in human genome with alignment-based and assembly-based approaches
- Challenges, Solutions, and Quality Metrics of Personal Genome Assembly in Advancing Precision Medicine
- NETBAGs: A Network-Based Clustering Approach with Gene Signatures for Cancer Subtyping Analysis
Showing 5 of 6 shared publications
Baitang Ning
National Center for Toxicological Research
6 shared publications
In database
- Study of pharmacogenomic information in FDA-approved drug labeling to facilitate application of precision medicine
- Long noncoding RNA LINC00844-mediated molecular network regulates expression of drug metabolizing enzymes and nuclear receptors in human liver cells
- MicroRNAs hsa-miR-495-3p and hsa-miR-486-5p suppress basal and rifampicin-induced expression of human sulfotransferase 2A1 (SULT2A1) by facilitating mRNA degradation
- Challenges, Solutions, and Quality Metrics of Personal Genome Assembly in Advancing Precision Medicine
- Coordinated Regulation of UGT2B15 Expression by Long Noncoding RNA LINC00574 and hsa-miR-129-5p in HepaRG Cells
Showing 5 of 6 shared publications
Erin L. Crawford
University of Toledo (US)
6 shared publications
- Technical advance in targeted NGS analysis enables identification of lung cancer risk-associated low frequency TP53, PIK3CA, and BRAF mutations in airway epithelial cells
- Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA
- Abstract 1623: Inter-laboratory harmonization of next generation sequencing somatic mutation assays for cancer response prediction
- Abstract 432: Novel method for NGS analysis of actionable mutations in circulating tumor DNA specimens: improved quality control and 20-fold lower sequencing required
- Abstract 4609: TP53, PIK3CA, and BRAF somatic mutations in airway epithelial field of injury associated with lung cancer risk
Showing 5 of 6 shared publications
Daniel J. Craig
University of Toledo (US)
6 shared publications
- A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency
- Technical advance in targeted NGS analysis enables identification of lung cancer risk-associated low frequency TP53, PIK3CA, and BRAF mutations in airway epithelial cells
- Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA
- Abstract 432: Novel method for NGS analysis of actionable mutations in circulating tumor DNA specimens: improved quality control and 20-fold lower sequencing required
- Abstract 4609: TP53, PIK3CA, and BRAF somatic mutations in airway epithelial field of injury associated with lung cancer risk
Showing 5 of 6 shared publications
Minjun Chen
National Center for Toxicological Research
5 shared publications
In database
- Integrating Drug’s Mode of Action into Quantitative Structure–Activity Relationships for Improved Prediction of Drug-Induced Liver Injury
- BERT-Based Natural Language Processing of Drug Labeling Documents: A Case Study for Classifying Drug-Induced Liver Injury Risk
- NETBAGs: A Network-Based Clustering Approach with Gene Signatures for Cancer Subtyping Analysis
- Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals
- Predicting the Risks of Drug-Induced Liver Injury in Humans Utilizing Computational Modeling
Shraddha Thakkar
United States Food and Drug Administration (US)
5 shared publications
- Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity
- Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA
- Comprehensive Assessments of RNA-seq by the SEQC Consortium: FDA-Led Efforts Advance Precision Medicine
- Study of pharmacogenomic information in FDA-approved drug labeling to facilitate application of precision medicine
- A framework enabling LLMs into regulatory environment for transparency and trustworthiness and its application to drug labeling document
Rebecca Kusko
5 shared publications
- Evaluating the analytical validity of circulating tumor DNA sequencing assays for precision oncology
- Toward best practice in cancer mutation detection with whole-genome and whole-exome sequencing
- A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency
- Advancing NGS quality control to enable measurement of actionable mutations in circulating tumor DNA
- Text summarization with ChatGPT for drug labeling documents
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