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Presence
Current · Arkansas
Last published
2025
Sources
OpenAlex · ORCID
Refreshed
2026-10-06
H
Hong Fang
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.
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Also affiliated: United States Food and Drug Administration (2001–2025); University of Health Sciences and Pharmacy (2025); Washington University in St. Louis (2025); Northrop Grumman (United States) (2004); ICF International (United States) (2008–2017); Procter & Gamble (United States) (2003); Zhejiang University (2010)
50 h-index
112 pubs
11,876 cited
Research Areas
Biomedical Subjects
Biography and Research Information
OverviewAI-generated summary
Hong Fang's research centers on the application and validation of molecular profiling technologies, particularly in the context of toxicological research and drug development. Fang was involved in the MicroArray Quality Control (MAQC) project, a significant initiative aimed at assessing the reproducibility and reliability of gene expression measurements across different platforms. This work led to publications in 2006 and 2010, focusing on inter- and intra-platform reproducibility and the development of predictive models using microarray data.
Further research has explored the concordance between different transcriptomic technologies, such as RNA-seq and microarrays, examining how factors like chemical treatment and transcript abundance influence their agreement, as detailed in a 2014 publication. Fang has also investigated structure-activity relationships for various chemical compounds, including natural, synthetic, and environmental estrogens and androgens, contributing to the understanding of potential toxicological effects. This includes work on chemicals binding to the androgen receptor and a focus on drug-induced liver injury, utilizing FDA-approved drug labeling information.
With a career marked by over 120 publications and a high citation count, Fang leads a research group at the National Center for Toxicological Research. Key collaborators include Leihong Wu, Joshua Xu, Weida Tong, and Lan Ying, all affiliated with the same institution. Fang's work emphasizes the development and validation of methodologies for robust biological data generation and interpretation.
Metrics
- h-index: 50
- Publications: 112
- Citations: 11,876
Selected Publications
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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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S02-03 FDALabel: enabling full text searching of drug labeling (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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Text summarization with ChatGPT for drug labeling documents (2024)
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RxBERT: Enhancing drug labeling text mining and analysis with AI language modeling (2023)
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Artificial intelligence and real-world data for drug and food safety – A regulatory science perspective (2023)
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FDALabel for drug repurposing studies and beyond (2020)
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Study of pharmacogenomic information in FDA-approved drug labeling to facilitate application of precision medicine (2020)
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Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA (2019)
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Drug-Induced Liver Injury (DILI) Classification and Its Application on Human DILI Risk Prediction (2018)
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Multiple microRNAs function as self-protective modules in acetaminophen-induced hepatotoxicity in humans (2017)
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The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury (2017)
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Integrating Drug’s Mode of Action into Quantitative Structure–Activity Relationships for Improved Prediction of Drug-Induced Liver Injury (2017)
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Potential Reuse of Oncology Drugs in the Treatment of Rare Diseases (2016)
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FDA drug labeling: rich resources to facilitate precision medicine, drug safety, and regulatory science (2016)
Collaboration Network
Top Collaborators
Weida Tong
National Center for Toxicological Research
68 shared publications
In database
- Toward interoperable bioscience data
- Structure−Activity Relationships for a Large Diverse Set of Natural, Synthetic, and Environmental Estrogens
- Study of 202 Natural, Synthetic, and Environmental Chemicals for Binding to the Androgen Receptor
- QSAR Models Using a Large Diverse Set of Estrogens
- A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data
Showing 5 of 68 shared publications
Huixiao Hong
National Center for Toxicological Research
33 shared publications
In database
- The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance
- Study of 202 Natural, Synthetic, and Environmental Chemicals for Binding to the Androgen Receptor
- A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data
- Mold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics
- Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models
Showing 5 of 33 shared publications
Roger Perkins
National Center for Toxicological Research (US)
31 shared publications
- Structure−Activity Relationships for a Large Diverse Set of Natural, Synthetic, and Environmental Estrogens
- Study of 202 Natural, Synthetic, and Environmental Chemicals for Binding to the Androgen Receptor
- QSAR Models Using a Large Diverse Set of Estrogens
- Mold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics
- Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models
Showing 5 of 31 shared publications
Leming Shi
National Center for Toxicological Research (US)
18 shared publications
- Structure−Activity Relationships for a Large Diverse Set of Natural, Synthetic, and Environmental Estrogens
- A comparison of batch effect removal methods for enhancement of prediction performance using MAQC-II microarray gene expression data
- Mold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics
- ArrayTrack--supporting toxicogenomic research at the U.S. Food and Drug Administration National Center for Toxicological Research.
- An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era
Showing 5 of 18 shared publications
Joshua Xu
National Center for Toxicological Research
17 shared publications
In database
- The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance
- An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era
- Assessing batch effects of genotype calling algorithm BRLMM for the Affymetrix GeneChip Human Mapping 500 K array set using 270 HapMap samples
- Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA
- An FDA bioinformatics tool for microbial genomics research on molecular characterization of bacterial foodborne pathogens using microarrays
Showing 5 of 17 shared publications
Zhenqiang Su
National Center for Toxicological Research (US)
16 shared publications
- The concordance between RNA-seq and microarray data depends on chemical treatment and transcript abundance
- Mold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics
- An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era
- Next-generation sequencing and its applications in molecular diagnostics
- Assessing batch effects of genotype calling algorithm BRLMM for the Affymetrix GeneChip Human Mapping 500 K array set using 270 HapMap samples
Showing 5 of 16 shared publications
Leming Shi
Fudan University (CN)
11 shared publications
- QSAR Models Using a Large Diverse Set of Estrogens
- Next-generation sequencing and its applications in molecular diagnostics
- k-Nearest neighbor models for microarray gene expression analysis and clinical outcome prediction
- Comparing Next-Generation Sequencing and Microarray Technologies in a Toxicological Study of the Effects of Aristolochic Acid on Rat Kidneys
- Technical Reproducibility of Genotyping SNP Arrays Used in Genome-Wide Association Studies
Showing 5 of 11 shared publications
Minjun Chen
National Center for Toxicological Research
11 shared publications
In database
- Toward Predictive Models for Drug-Induced Liver Injury in Humans: Are we There Yet?
- Quantitative Structure-Activity Relationship Models for Predicting Drug-Induced Liver Injury Based on FDA-Approved Drug Labeling Annotation and Using a Large Collection of Drugs
- A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model
- The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury
- atBioNet– an integrated network analysis tool for genomics and biomarker discovery
Showing 5 of 11 shared publications
Weigong Ge
National Center for Toxicological Research
10 shared publications
In database
- Mold2, Molecular Descriptors from 2D Structures for Chemoinformatics and Toxicoinformatics
- An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era
- Assessing batch effects of genotype calling algorithm BRLMM for the Affymetrix GeneChip Human Mapping 500 K array set using 270 HapMap samples
- Comparing Next-Generation Sequencing and Microarray Technologies in a Toxicological Study of the Effects of Aristolochic Acid on Rat Kidneys
- Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA
Showing 5 of 10 shared publications
Reagan Kelly
National Center for Toxicological Research (US)
10 shared publications
- Quantitative Structure-Activity Relationship Models for Predicting Drug-Induced Liver Injury Based on FDA-Approved Drug Labeling Annotation and Using a Large Collection of Drugs
- Translating Clinical Findings into Knowledge in Drug Safety Evaluation - Drug Induced Liver Injury Prediction System (DILIps)
- Investigating drug repositioning opportunities in FDA drug labels through topic modeling
- A phenome-guided drug repositioning through a latent variable model
- atBioNet– an integrated network analysis tool for genomics and biomarker discovery
Showing 5 of 10 shared publications
Leihong Wu
National Center for Toxicological Research
10 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 10 shared publications
Baitang Ning
National Center for Toxicological Research
8 shared publications
In database
- An investigation of biomarkers derived from legacy microarray data for their utility in the RNA-seq era
- Next-generation sequencing and its applications in molecular diagnostics
- Comparing Next-Generation Sequencing and Microarray Technologies in a Toxicological Study of the Effects of Aristolochic Acid on Rat Kidneys
- Technical Reproducibility of Genotyping SNP Arrays Used in Genome-Wide Association Studies
- Study of pharmacogenomic information in FDA-approved drug labeling to facilitate application of precision medicine
Showing 5 of 8 shared publications
Stephen Harris
National Center for Toxicological Research (US)
8 shared publications
- ArrayTrack--supporting toxicogenomic research at the U.S. Food and Drug Administration National Center for Toxicological Research.
- Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA
- FDA drug labeling: rich resources to facilitate precision medicine, drug safety, and regulatory science
- atBioNet– an integrated network analysis tool for genomics and biomarker discovery
- FDALabel for drug repurposing studies and beyond
Showing 5 of 8 shared publications
William S. Branham
National Center for Toxicological Research (US)
7 shared publications
- Structure−Activity Relationships for a Large Diverse Set of Natural, Synthetic, and Environmental Estrogens
- Study of 202 Natural, Synthetic, and Environmental Chemicals for Binding to the Androgen Receptor
- QSAR Models Using a Large Diverse Set of Estrogens
- Human Sex Hormone-Binding Globulin Binding Affinities of 125 Structurally Diverse Chemicals and Comparison with Their Binding to Androgen Receptor, Estrogen Receptor, and α-Fetoprotein
- Microarray analysis of virulence gene profiles in Salmonella serovars from food/food animal environment
Showing 5 of 7 shared publications
Carrie L. Moland
National Center for Toxicological Research
6 shared publications
In database
- Structure−Activity Relationships for a Large Diverse Set of Natural, Synthetic, and Environmental Estrogens
- Study of 202 Natural, Synthetic, and Environmental Chemicals for Binding to the Androgen Receptor
- QSAR Models Using a Large Diverse Set of Estrogens
- Human Sex Hormone-Binding Globulin Binding Affinities of 125 Structurally Diverse Chemicals and Comparison with Their Binding to Androgen Receptor, Estrogen Receptor, and α-Fetoprotein
- Changes in expression level of genes as a function of time of day in the liver of rats
Showing 5 of 6 shared publications
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