Leihong Wu Ph.D.

Leadership Role

Bioinformatician — Division of Bioinformatics and Biostatistics

Leihong Wu, Ph.D.
(870) 543-7121
NCTRResearch@fda.hhs.gov

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Background

Dr. Leihong Wu received his bachelor’s degree in bioinformatics in 2008 from Zhejiang University in China. He then received his Ph.D. degree in pharmacology from Zhejiang University in 2013. In the same year, he joined Division of Bioinformatics and Biostatistics at NCTR as an ORISE postdoctoral fellow and, in 2017, he officially joined NCTR as visiting scientist. Dr. Wu has published over 30 peer-reviewed journal articles, with more than 10 publications as the first or corresponding author.

Since joining NCTR, he has received the following awards:

NCTR Group Recognition Award (2017)
NCTR Chief Scientist Publication Award for Junior Scientist (2018)
First-place poster award in the First Annual NCTR Science Forum (2018)

Research Interests

Dr. Wu’s research interest is to apply bioinformatics — particularly, Artificial Intelligence (AI) and Machine Learning (ML) — to biomedical research and informatics. Specifically, Dr. Wu’s work has focused on the development of algorithms for biological and pharmaceutical research tasks such as drug safety, QSAR modeling, and genomics. Dr. Wu’s research addresses some of the most pressing issues in understanding and applying novel bioinformatics database tools and frameworks that enhance the accuracy, safety, and efficiency of drug discovery, repositioning, and efficacy studies.

His current interests focus on developing AI/machine learning algorithms in various drug- and food-associated research areas including hepatotoxicity, genomics, and text mining. Dr. Wu’s current research interests include:

Developing innovative AI algorithms for big data analysis, including multi-platform biological data such as gene expression, sequencing, and bioassays
Designing and developing AI/machine learning framework to facilitate regulatory science
Developing advanced predictive models using deep learning for biomarker identification of DILI and predictive toxicology research
Developing innovative machine learning algorithms for text-mining in massive FDA regulatory documents
Developing convolutional neural network architectures for advanced imaging analysis in drug and food safety assessment
Designing and developing databases and visualization tools that promoting AI for regulatory use, including FDALabel

Professional Societies/National and International Groups

Arkansas Bioinformatics Consortium
Member
2015 – Present

International Society for Computational Biology
Member
2017 – 2019

MidSouth Computational Biology and Bioinformatics Society
Member
2015 – Present

Select Publications

Publication titles are linked to text abstracts on PubMed.

DLI-IT: A Deep Learning Approach to Drug Label Identification Through Image and Text Embedding.
Liu X., Meehan J., Tong W., Wu L., Xu X., and Xu J.
BMC Med Inform Decis Mak. 2020, 20, 1-9. doi: 10.1186/s12911-020-1078-3.

Technical Advance in Targeted NGS Analysis Enables Identification of Lung Cancer Risk-associated Low Frequency TP53, PIK3CA, and BRAF Mutations in Airway Epithelial Cells.
Craig D.J., Morrison T., Khuder S.A., Crawford E.L., Wu L., Xu J., Blomquist T.M., and Willey J.
BMC Cancer. 2020, 19 (1), 1081. Doi: 10.1186/s12885-019-6313-x

A Deep Learning Model to Recognize Food Contaminating Beetle Species Based on Elytra Fragments.
Wu L., Liu, Z. Bera T., Ding H., Langley D.A., Jenkins-Barnes A., Furlanello C., Maggio V., Tong W., and Xu J.
Comput Electron Agric. 2019, 166, 105002; doi: 10.1016/j.compag.2019.105002.

HetEnc: A Deep Learning Predictive Model for Multi-Type Biological Dataset.
Wu L., Liu X., and Xu, J.
BMC Genom. 2019, 20 (1), 1-10. doi: 10.1186/s12864-019-5997-2.

Study of Serious Adverse Drug Reactions Using FDA-Approved Drug Labeling and MedDRA.
Wu L., Ingle T., Liu Z., Zhao-Wong A., Harris S., Thakkar S., Zhou G., Yang J., Xu J., Mehta D., Ge W., Tong W., and Fang H.
BMC Bioinform. 2019, 20 (2), 97. doi: 10.1186/s12859-019-2628-5.

Integrating Drug’s Mode of Action into Quantitative Structure–Activity Relationships for Improved Prediction of Drug-Induced Liver Injury.
Wu L., Liu Z., Auerbach S., Huang R., Chen M., McEuen K., Xu J., Fang H., and Tong, W.
J Chem Inf Model. 2017, 57 (4), 1000-1006; doi: 10.1021/acs.jcim.6b00719. Epub 2017 Apr 10.

Direct Comparison of Performance of Single Nucleotide Variant Calling in Human Genome with Alignment-Based and Assembly-Based Approaches.
Wu L., Yavas G., Hong H., Tong W., and Xiao W.
Sci Rep. 2017, 7 (1), 10963. doi: 10.1038/s41598-017-10826-9.

NETBAGs: A Network-Based Clustering Approach with Gene Signatures for Cancer Subtyping Analysis.
Wu L., Liu Z., Xu J., Chen M., Fang H., Tong W., and Xiao W.
Biomark Med. 2015, 9 (11), 1053-1065. doi: 10.2217/bmm.15.96. Epub 2015 Oct 26.

Contact Information: Leihong Wu; 870-543-7121

Expertise: Expertise

Approach

Domain

Technology & Discipline

Bioinformatics

Biostatistics