Health Information Scientist — Office of Scientific Coordination

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Hong Fang, Ph.D.
(870) 543-7121
NCTRResearch@fda.hhs.gov  

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About  |  Publications 

Background

Dr. Hong Fang received a bachelor’s degree in chemistry followed by a master’s degree in chemistry from Fudan University in Shanghai, China. She then received a Ph.D. degree in chemistry from the University of Missouri, St. Louis. Dr. Fang worked for the St. Louis County Police Department Crime Laboratory as a forensic scientist before being hired at FDA’s National Center for Toxicological Research (NCTR) as a postdoctoral fellow in 1997. She took a position as a contractor for Northrop Grumman Information Technology working as an NCTR senior computational scientist until 2004 when she was hired as a contractor for Z-Tech Corporation as a senior bioinformatician at NCTR. In 2008, Dr. Fang was appointed by ICF International as Director of their NCTR Bioinformatics Group. In 2012, Dr. Fang was hired as a full-time FDA employee and has served as a senior bioinformatician and project manager for NCTR. Since 2016 she has served as an adjunct professor of the Department of Information Science for the University of Arkansas at Little Rock. Currently, she is a senior Health Information Scientist and leading the large collaborative FDALabel program across FDA centers as well as at the public.

Dr. Fang has received many awards over the years including:

• 2020 – “Outstanding Inter-center Scientific Collaboration” FDA Chief Scientific Achievement Team for FDA Liver Toxicity Working Group, cited as "The liver toxicity working promotes interdisciplinary collaborations to improve risk management of FDA regulated products with a potential for drug-induced liver injury"
• 2019 – NCTR Director's Award (Team), "For exemplary leadership in managing the FDA/NCTR Scientific Achievement Award Program"
• 2017 – FDA Chief Scientist Publication Award for basic translational or applied science: "Potential Reuse of Oncology Drugs in the Treatment of Rare Diseases" Trends in Pharmacological Sciences, 2016, 843-857
• 2016 – “Outstanding Inter-center Scientific Collaboration” Group Recognition Award for FDALabel team from FDA/the Department of Health and Human Services (HHS), cited as “Developed a bioinformatics tool and relational database for FDA drug labeling to aid regulatory decision making and drug review in advancing translational and regulatory sciences”
• 2016 – Commissioner’s Special Citation “Research to the Review and Return (R2R) Team for a Cross Center Bioinformatics Projects Benefiting Regulatory Business Processes” Group Recognition Award from FDA.
• 2016 – Chief Scientist Publication Award for “DataMethods/Analysis/Study Design” (Group)
• 2015 – FDA Chief Scientist Publication Award for basic, translational, or applied science
• 2015 – NCTR Scientific Achievement Award – Excellence in Analytical Science (Group)/FDA Team of the Sequencing Quality Control project
• 2013 – FDA Group Recognition (Crosscutting) Award “For inter-center development of a custom microarray genomics pipeline for food borne enteric pathogen outbreak investigations”
• 2009 – Z-Tech/ICF International Bioinformatics Group, Group Recognition Award from FDA/HHS. for the achievement in development a suite of NCTR bioinformatics tools
• 2009 – The FDA MicroArray Quality Control (MAQC) Project Committee, Leveraging Collaboration Award FDA
• 2005 – NCTR ArrayTrack Development Team, Commissioner’s Special Citation awarded by FDA

Research Interests

Dr. Fang’s main research interest is to apply bioinformatics, chemoinformatics, knowledge base and big data methodologies, and predictive toxicology approaches to address issues in the areas of:

• Data science
• Bioinformatics
• Drug labeling
• Medical informatics
• Health informatics
• Databases
• Toxicology
• Clinical applications
• Data-oriented research (data mining)
• Adverse drug reactions (ADR)
• Pharmacogenomics and precision medicine
• Biomarker development and application

Dr. Fang has over 15 years of experience applying data mining, pattern recognition, machine learning, classification, molecular modeling, bioinformatics, and chemoinformatics approaches in these areas. Her research experience is broad, ranging from applying bioinformatics methods to studying specific diseases and toxicity to managing and coordinating large software development programs. For example, Dr. Fang developed integrated bioinformatics approaches for biomarker discovery for lupus, chronic fatigue syndrome, drug-induced liver injury, cancer, brain function, and more. Currently, she is leading an effort to develop the FDALabel database — a web-based application — to query and mine FDA drug labeling data. One goal of this project is to integrate FDA labeling data with various medical ontologies, such as the Medical Dictionary for Regulatory Application (MedDRA) and the Unified Medical Language System (UMLS) to enhance the use of drug labeling data and its readiness for integration with other FDA databases such as the FDA Adverse Event Reporting System.

As a senior health information scientist and project manager within NCTR’s Office of Scientific Coordination Dr. Fang leads several large bioinformatics projects. She has led and coordinated efforts that resulted in widely used software to support FDA‘s drug safety, genomic research, review, study of adverse drug reaction (ADR), and precision medicine. Some of the bioinformatics tools that she helped develop include:

• FDALabel: a full-text search web-based database of the FDA drug labeling system which is a resource in study of drug safety, pharmacovigilance, and precision medicine
• ArrayTrack: an integrated genomics tool to support FDA review and research on genomics and pharmacogenomics
• LTKB (Liver Toxicity Knowledge Base): a collection of diverse drug-induced liver injury data to assess risk of drug-induced liver injury
• EDKB (Endocrine Disruptor Knowledge Base): an integrated database to prioritize chemicals for potential endocrine disruption

Dr. Fang has mentored and supervised many young scientists, postdoctoral fellows, and summer interns – one student each year for six consecutive years. She also has many publications that are widely recognized with high citations – a total of 14,765 citations from 152 manuscripts and an H-Index of 57, as of July 2021, from Google Scholar Citations.

Professional Societies/National and International Groups

Journal of Frontiers in Neurology
Editorial Board Member
2014 – Present

MidSouth Computational Biology & Bioinformatics Society (MCBIOS)
Member
2008 – Present

Society of Toxicology (SOT)
Member
2008 – Present

Select Publications

FDALabel for Drug Repurposing Studies and Beyond.
Fang H., Harris S.C., Liu Z., Thakkar S., Yang J., Ingle T., Xu J., Lesko L., Rosario L., and Tong W.
Nature Biotechnology. 2020, 38:1378-1379.

Study of Pharmacogenomic Information in FDA-approved Drug Labeling to Facilitate Application of Precision Medicine.
Mehta D., Uber R., Ingle T., Li C., Liu Z., Thakkar S., Ning B., Wu L., Yang J., Harris S.C., Zhou G., Xu J., Tong W., Lesko L., and Fang H.
Drug Discovery Today. 2020, 25(5): 813-820.
 
Potential Reuse of Oncology Drugs in the Treatment of Rare Diseases.
Liu Z., Fang H., Slikker W., and Tong W.
Trends in Pharmacological Sciences. 2016, 843–857.
 
FDA Drug Labeling: Rich Resources to Facilitate Precision Medicine, Drug Safety, and Regulatory Science.
Fang H., Harris S., Liu Z., Zhou G., Zhang G., Xu J., Howard P., and Tong W.
Drug Discovery Today. 2016, 21(10): 1566-1570.
 
Exploring the FDA Adverse Event Reporting System to Generate Hypotheses for Monitoring of Disease Characteristics.
Fang H., Su Z., Wang Y., Miller A., Liu Z., Howard P., Tong W., and Lin S.M.
Clinical Pharmacology & Therapeutics. 2014, 95(5):496-498.
 
A Comprehensive Assessment of RNA-Seq Accuracy, Reproducibility and Information Content by the Sequence Quality Control Consortium.
SEQC/MAQC-III Consortium.
Nature Biotechnology. 2014, 32: 903–914.
 
An Investigation of Biomarkers Derived from Legacy Microarray Data for their Utility in the RNA-Seq Era.
Su Z., Fang H., Hong H., Shi L., Zhang W., Zhang W., Zhang Y., Dong Z., Lancashire L.J., and Bessarabova M.
Genome Biology. 2014, 15(12):1.
 
Towards Interoperable Bioscience Data.
Sansone S.A., Rocca-Serra P., Field D., Maguire E., Taylor C., Hide W., Hofmann O., Fang H., Neumann S., and Tong W.
Nature Genetics. 2012, 44(2):121-126.
 
Next-Generation Sequencing and its Applications in Molecular Diagnostics.
Su Z., Ning B., Fang H., and Hong H.
Molecular Diagnostics. 2011, 11(3):333-343.
 
Meta-Analysis of Microarray Data Using a Pathway-Based Approach Identifies a 37-Gene Expression Signature for Systemic Lupus Erythematosus in Human Peripheral Blood Mononuclear Cells.
Arasappan D., Tong W., Siegel J., Mummaneni P., Fang H., and Amur S.
BMC Medicine. 2011, 9:65.
 
An FDA Bioinformatics Tool for Microbial Genomics Research on Molecular Characterization of Bacterial Foodborne Pathogens Using Microarrays.
Fang H., Xu J., Ding D., Jackson S., and Patel I.
BMC Bioinformatics. 2010, 11(Suppl 6):S4.
 
The MicroArray Quality Control (MAQC)-II Study of Common Practices for the Development and Validation of Microarray-Based Predictive Models.
The MAQC Consortium.
Nature Biotechnology. 2010, 28:827–838.
 
GOFFA: Gene Ontology for Functional Analysis- Software for Gene Ontology-Based Functional Analysis of Genomic and Proteomic Data.
Sun H., Fang H., Chen T., Perkins R., and Tong W.
BMC Bioinformatics. 2006, 7(Suppl 2):S23.
 
The MicroArray Quality Control (MAQC) Project Shows Inter- and Intraplatform Reproducibility of Gene Expression Measurements.
The MAQC Consortium.
Nature Biotechnology. 2006, 24(9) and 24(10s):1151-1161.
 
Performance Comparison of One-Color and Two-Color Platforms Within the Microarray Quality Control (MAQC) Project.
Patterson T., Lobenhofer E., Fulmer-Smentek S., Collins J., Chu T., Bao W., and Fang H.
Nature Biotechnology. 2006, 24(9) and 24(10s):1140–1150.
 
Gene Expression Profile Exploration of a Large Dataset on Chronic Fatigue Syndrome.
Fang H., Xie Q., Boneva R., Fostel J., Perkins R., and Tong W.
Pharmacogenomics. 2006, 7(3).
 
Bioinformatics Approaches for Cross-Species Liver Cancer Analysis Based on Microarray Gene Expression Profiling.
Fang H., Tong W., Perkins R., Shi L., Hong H., Cao X., Xie Q., Yim S., Ward J., and Pitot H.
BMC bioinformatics. 2005, 6(2):1.
 
ArrayTrack-Supporting Toxicogenomic Research at the FDA’s National Center for Toxicological Research (NCTR).
Tong W., Cao X., Harris S., Sun H., Fang H., Fuscoe J., Harris A., Hong H., Xie Q., Perkins R., Shi L., and Casciano D.
EHP Toxicogenomics. 2003, 111(15):1819-1826.
 
Study of 202 Natural, Synthetic, and Environmental Chemicals for Binding to the Androgen Receptor.
Fang H., Tong W., Branham W.S., Moland C.L., Dial S.L., Hong H., Xie Q., Perkins R., Owens W., and Sheehan D.M.
Chem Res Toxicol. 2003, 16(10):1338-58, 2003.
 
Decision Forest: Combining the Predictions of Multiple Independent Decision Tree Models.
Tong W., Hong H., Fang H., Xie Q., and Perkins R.
J. Chem. Info. Comp. Sci. 2003, 43(2):525 – 531.
 
Structure-Activity Relationship for a Large Diverse Set of Natural, Synthetic and Environmental Chemicals.
Fang H., Tong W., Shi L., Blair R., Perkins R., Branham W., Dial S., Moland C., and Sheehan, D.
Chemical Research in Toxicology. 2001, 14:280–294.