Supervisory Computer Scientist — Division of Bioinformatics and Biostatistics

Joshua Xu, Ph.D.
(870) 543-7121
[email protected]  

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 About  |  Publications  |  Lab Members

Background

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, an R&D 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:  1) data analysis, 2) bioinformatics method development, and 3) design and development of bioinformatics tools and systems to manage and analyze genomics 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

Research Interests

Dr. Xu’s experience includes about 20 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, genomics data analysis, image analysis, high-performance computing, and artificial intelligence. 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 onco-panel sequencing, liquid biopsy, genomics, bioimaging data analysis, text mining, and artificial intelligence. 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 oncopanel sequencing, including liquid biopsy. Oncopanel sequencing targets some small regions of the genome and can detect 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 oncopanels and 30 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
Member
2015 – Present

American Statistical Association
Member
2020 — Present

The MidSouth Computational Biology and Bioinformatics Society
Member
2009 – Present

Select Publications

Publication titles are linked to text abstracts on PubMed.

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., Zhou G., Xu J., Tong W., Lesko L., and Fang H.
Drug Discov Today. 2020, 25(5):813-820. doi: 10.1016/j.drudis.2020.01.023.
 

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., Xu J.
BMC Medical Inform Decis Mak. 2020, 20:68.
 

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.
 

Liquid Biopsy and its Role in an Advanced Clinical Trial for Lung Cancer.
Johann D.J., Steliga M., Shin I.J., Yoon D., Arnaoutakis K., Hutchins L., Liu M., Liem J., Walker K., Pereira A., Yang M., Jeffus S.K., Peterson E., and Xu J.
Exp Biol Med. 243. 262-271.
 

Comparing SVM and ANN Based Machine Learning Methods for Species Identification of Food Contaminating Beetles.
Bisgin H., Bera T., Ding H., Semey H.G., Wu L., Liu Z., Barnes A.E., Langley D.A., Pava-Ripoll M., Vyas H.J., Tong W., and Xu J.
Sci Rep 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.
 

The FDA’s Experience with Emerging Genomics Technologies—Past, Present, and Future.
Xu J., Thakkar S., Gong B., and Tong W.
AAPS Journal. 2016, 18(4): 814–818.
 

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 Biol. 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 Biol. 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 Biotechnol. 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.
 

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.
Nat Comm. 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.
Hum Genomics. 2012, 6:5.

Lab Members

Dr. Tanmay Bera
Postdoc
(870) 543-7391
[email protected]