Computer Scientist — Division of Bioinformatics and Biostatistics
Joshua Xu, Ph.D.
After graduating with a Ph.D. in electrical engineering from Texas A&M University in 1999, Dr. Xu worked as a senior software engineer for a congressionally funded mobile telemedicine program at the Texas Center for Applied Technology, a research and development center of the Texas A&M University System. In this position he designed and developed many vital modules through software development and hardware integration. In 2007, he joined ICF International to work as an onsite contractor for the National Center for Toxicological Research. Dr. Xu’s primary responsibilities included:
design and development of bioinformatics tools systems to manage and analyze:
- genomics data
- genetics data
In 2012, Dr. Xu joined the newly formed Division of Bioinformatics and Biostatistics to focus on genomics and image analysis. In 2018, he became the Branch Chief for Research-to-Review and Return (R2R) in the Division. Below is a list of awards and recognitions Dr. Xu has received:
2018 NCTR Science Forum Poster-Presentation Award for “Deep Learning for Food Contamination Detection”
2016 FDA Commissioner’s Special Citation for “Cross-Center Bioinformatics Projects Benefiting Regulatory Business Processes”
2016 FDA Outstanding Inter-Center Scientific Collaboration (group) Award for “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 FDA Chief Scientist Publication Award (group) for “Data Methods, Analysis, and Study Design”
2015 NCTR Excellence in Analytical Science Award for “Sequencing Quality Control Project”
2015 FDA Chief Scientist Publication Award (group) for “Basic, Translational or Applied Science”
2015 FDA Group Recognition Award for “Patient Narrative Tool Development Team”
2015 FDA Leveraging/Collaboration Award for “Food Contamination Detection via Bioimaging”
2015 NCTR Outstanding Service Award
2014 NCTR Cash Award for “Special Bioinformatics Services”
2013 FDA Group Recognition Award for “Food Pathogen Analytics Group” (CFSAN/NCTR)
2009 FDA Group Recognition Award for “Z-Tech/ICF International Bioinformatics Group”
Dr. Xu’s experience includes about 16 years developing telemedicine systems, bioinformatics software and systems. He has been working closely with the FDA’s Voluntary eXploratory Data Submission program to review and analyze the submissions involving genetic data and personalized medicine. He specializes in:
software design and implementation
database design and development
signal and image processing algorithm development
high performance computing
analyzing data to associate disease and toxicity with genetic profiles.
Dr. Xu’s experience includes more than 17 years developing bioinformatics software and systems, and conducting bioinformatics research. He has worked closely with the FDA’s Voluntary eXploratory Data Submission program to review and analyze the submissions involving pharmacogenomics, genetic data, and personalized medicine. Dr. Xu has in-depth expertise and experience in software design and development, data mining, data integration, next-generation sequencing data analysis, image analysis, high-performance computing, and analyzing data to associate disease and toxicity with genetic profiles. He has led several systems-development projects at NCTR including SNPTrack—an integrated solution for managing, analyzing, and interpreting genetic association study data. His recent endeavor has been with the Sequencing Quality Control (SEQC2) project, a large and international collaborative consortium led by FDA to evaluate the technical reliabilities and scientific applications of the next generation sequencing (NGS) technologies.
Dr. Xu’s research interests lie in targeted deep sequencing, genomics, bioimaging data analysis, text mining, and machine learning. As the principle investigator he is leading a large working group as part of the SEQC2 consortium to assess the reproducibility and detection sensitivity of onco-panel sequencing, including liquid biopsy. Onco-panel sequencing targets a few small regions of the genome and is capable of detecting rare, but clinically relevant, sub-clonal mutations. Accurate diagnosis and subsequent tailoring of therapy depends on thorough characterization of tumor mutational spectra. A cross-lab evaluation of eight pan-cancer comprehensive panels and five circulating-tumor DNA liquid-biopsy assays is currently underway. The working group has over 200 participants from academia, government agencies, and industry (including eight companies providing onco-panels and 29 testing laboratories). The scope and complexity of this comprehensive study is unprecedented and aims to provide recommendation in support for FDA’s mission in regulatory oversight of NGS diagnostic tests.
Professional Societies/National and International Groups
Arkansas Bioinformatics Consortium
2015 – Present
The MidSouth Computational Biology and Bioinformatics Society
2009 – Present
Publication titles are linked to text abstracts on PubMed.
Liquid Biopsy and its Role in an Advanced Clinical Trial for Lung Cancer.
Donald J Johann, Mathew Steliga, Ik J Shin, Donghoon Yoon, Konstantinos Arnaoutakis, Laura Hutchins, Meeiyueh Liu, Jason Liem, Karl Walker, Andy Pereira, Mary Yang, Susanne K Jeffus, Erich Peterson, and Joshua Xu.
Experimental Biology and Medicine. 243. 262-271. 10.1177/1535370217750087.
Comparing SVM and ANN Based Machine Learning Methods for Species Identification of Food Contaminating Beetles.
Halil Bisgin, Tanmay Bera, Hongjian Ding, Howard G. Semey, Leihong Wu, Zhichao Liu, Amy E. Barnes, Darryl A. Langley, Monica Pava-Ripoll, Himansu J. Vyas, Weida Tong and Joshua Xu.
Scientific Reports 8: 6532, 2018.
Species Identification of Food Contaminating Beetles by Recognizing Patterns in Microscopic Images of Elytra Fragments.
Park S., Bisgin H., Ding H., Semey H., Langley D., Tong W., and Xu J.
PLoS ONE. 2016, 11(6): e0157940.
An Image Analysis Environment for Species Identification of Food Contaminating Beetles.
Martin D., Ding H., Wu L., Semey H., Barnes A., Langley D., Park S., Liu Z., Tong W., and Xu J.
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16). 2016, pp. 4375-4376.
Comparison Of RNA-Seq And Microarray-Based Models For Clinical Endpoint Prediction.
Zhang W., Yu Y., Hertwig F., Thierry-Mieg J., Zhang W., Thierry-Mieg D., Wang J., Furlanello C., Devanarayan V., Cheng J., Deng Y., Hero B., Hong H., Jia M., Li L., Lin S., Nikolsky Y., Oberthuer A., Qing T., Su Z., Volland R., Wang C., Wang M., Ai J., Albanese D., Asgharzadeh S., Avigad S., Bao W., Bessarabova M., Brilliant M., Brors B., Chierici M., Chu T., Zhang J., Grundy R., He M., Hebbring S., Kaufman H., Lababidi S., Lancashire L., Li Y., Lu X., Luo H., Ma X., Ning B., Noguera R., Peifer M., Phan J., Roels F., Rosswog C., Shao S., Shen J., Theissen J., Tonini G., Vandesompele J., Wu P., Xiao W., Xu J., Xu W., Xuan J., Yang Y., Ye Z., Dong Z., Zhang K., Yin Y., Zhao C., Zheng Y., Wolfinger R., Shi T., Malkas L., Berthold F., Wang J., Tong W., Shi L., Peng Z., and Fischer M.
Genome Biology. 2015, 16(6):133.
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., Bessarabova M., Yang X., Ning B., Gong B., Meehan J., Xu J., Ge W., Perkins R., Fischer M., and Tong W.
Genome Biology. 2014, 15(12):523.
The Concordance Between RNA-Seq And Microarray Data Depends On Chemical Treatment And Transcript Abundance.
Wang C., Gong B., Bushel P., Thierry-Mieg J., Thierry-Mieg D., Xu J., Fang H., Hong H., Shen J., Su Z., Meehan J., Li X., Yang L., Li H., Labaj P., Kreil D., Megherbi D., Gaj S., Caiment F., van Delft J., Kleinjans J., Scherer A., Devanarayan V., Wang J., Yang Y., Qian H., Lancashire L., Bessarabova M., Nikolsky Y., Furlanello C., Chierici M., Albanese D., Jurman G., Riccadonna S., Filosi M., Visintainer R., Zhang K., Li J., Hsieh J., Svoboda D., Fuscoe J., Deng Y., Shi L., Paules R., uerbach S., and Tong W.
Nat Biotechnology. 2014, 32: 926-932.
A Comprehensive Assessment Of RNA-Seq Accuracy, Reproducibility And Information Content By The Sequencing Quality Control Consortium.
SEQC/MAQC-III Consortium, Nat Biotechnology. 2014, 32: 903-914.
Cross-Platform Ultradeep Transcriptomic Profiling Of Human Reference RNA Samples By RNA-Seq.
Xu J., Su Z., Hong H., Thierry-Mieg J., Thierry-Mieg D., Kreil D., Mason C., Tong W., and Shi L.
Scientific Data 1. 2014, DOI: 10.1038/sdata.2014.20
Assessing Technical Performance in Differential Gene Expression Experiments with External Spike-in RNA Control Ratio Mixtures.
Munro S., Lund S., Pine P., Binder H., Clevert D., Conesa A., Dopazo J., Fasold M., Hochreiter S., Hong H., Jafari N., Kreil D., Labaj P., Li S., Liao Y., Lin S., Meehan J., Mason C., Santoyo-Lopez J., Setterquist R., Shi L., Shi W., Smyth G., Stralis-Pavese N., Su Z., Tong W., Wang C., Wang J., Xu J., Ye Z., Yang Y., Yu Y., and Salit M.
Nature Communications. 2014, DOI: 10.1038/ncomms6125
SNPTrackTM - An Integrated Bioinformatics System for Genetic Association Studies.
Xu J., Kelly R., Zhou G., Turner S., Ding D., Harris S., Hong H., Fang H., and Tong W.
Human Genomics. 2012, 6:5.
A Numerical Approach to the Selection of Basis for Frame-encoded MRI.
Magnetic Resonance Imaging. 2004, 22: 47-54.
Encoding with Frames in MRI and Analysis of Signal-to-Noise Ratio (SNR).
Xu Z., and Chan A.
IEEE Trans. Medical Imaging. 2002, 21: 332-342.
Dr. Tanmay Bera
- Contact Information
- Joshua Xu
- (870) 543-7391