Leihong Wu, Ph.D.
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. After that, he became an ORISE postdoctoral fellow at NCTR from 2014-2017. Dr. Wu officially joined NCTR as visiting scientist in January 2017 in the Division of Bioinformatics and Biostatistics. 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)
Dr. Wu has made substantial contributions to the field of bioinformatics. 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 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 analysis tools and standard procedures for Next Generation Sequencing (NGS) applications, especially establishing quality metrics for mutation detection of in deep sequencing technologies
- 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
- Applying machine learning algorithms for NGS data analysis, including mRNA, miRNA and lncRNA
- Designing and developing databases and visualization tools that promoting AI for regulatory use, including FDALabel
Professional Societies/National and International Groups
Arkansas Bioinformatics Consortium
2015 – Present
International Society for Computational Biology
2017 – 2019
MidSouth Computational Biology and Bioinformatics Society
2015 – Present
Publication titles are linked to text abstracts on PubMed.
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.
Computers and Electronics in Agriculture. 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 Genomics. 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 Bioinformatics. 2019, 20 (2), 97. doi: 10.1186/s12859-019-2628-5.
Multiple MicroRNAs Function as Self-Protective Modules in Acetaminophen-Induced Hepatotoxicity in Humans.
Yu D.*, Wu L.*, Gill P., Tolleson W.H., Chen S., Sun J., Knox B., Jin Y., Xiao W., Hong H., Wang Y., Ren Z., Guo L., Mei N., Guo Y., Yang X., Shi L., Chen Y., Zeng L., Dreval K., Tryndyak V., Pogribny I., Fang H., Shi T., McCullough S., Bhattacharyya S., Schnackenberg L., Mattes W., Beger R.D., James L., Tong W., and Ning, B.
Archives of Toxicology. 2018, 92 (2), 845-858. doi: 10.1007/s00204-017-2090-y.
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.
Journal of Chemical Information and Modeling. 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.
Scientific Reports. 2017, 7 (1), 10963. doi: 10.1038/s41598-017-10826-9.
Challenges, Solutions, and Quality Metrics of Personal Genome Assembly in Advancing Precision Medicine.
Xiao W., Wu L., Yavas G., Simonyan V., Ning B., and Hong H.
Pharmaceutics. 2016, 8 (2), 15. doi: 10.3390/pharmaceutics8020015.
Comprehensive Assessments of RNA-seq by the SEQC Consortium: FDA-Led Efforts Advance Precision Medicine.
Xu J., Gong B., Wu L., Thakkar S., Hong H., and Tong W.
Pharmaceutics. 2016, 8 (1), 8. doi: 10.3390/pharmaceutics8010008.
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.
Biomarkers in Medicine. 2015, 9 (11), 1053-1065. doi: 10.2217/bmm.15.96. Epub 2015 Oct 26.
- Contact Information
- Leihong Wu
ExpertiseApproachDomainTechnology & DisciplineBioinformaticsBiostatistics