Digital Biology Laboratory (DBL) is a research and education powerhouse in bioinformatics and computational biology. DBL works on development of novel computational methods, algorithms, software and information systems, as well as on broad applications of these tools and other informatics resources for various biological and medical problems. We have conducted research in many areas of computational biology and bioinformatics, including protein structure prediction and modeling, protein post-translational modifications, protein/RNA localization prediction, single-cell data analyses, computational systems biology, and bioinformatics applications in human, microbes, and plants. DBL is adaptable and able to recognize new opportunities and explore new areas in bioinformatics, maintaining a cutting-edge and novel research program. In recent years, DBL focuses on the interface between bioinformatics and deep learning.
Research at DBL has been supported by NIH, NSF, DOE, USDA, US Army, US Geological Survey, United Soybean Board, Missouri Soybean Merchandising Council, Missouri Life Science Trust Fund, Monsanto Research Fund, Cerner, and National Center for Soybean Biotechnology.

Recent Publication Highlight

  1. Ming Liu, Dongpeng Liu, Guangyu Sun, Yi Zhao, Duolin Wang, Fangxing Liu, Xiang Fang, Qing He, and Dong Xu, Detection of Malfunctional Smart Electricity Meters Based on Deep Learning. IEEE Industrial Electronics Magazine. In press.
  2. Yuexu Jiang, Yanchun Liang, Duolin Wang, Dong Xu, Trupti Joshi. A Dynamic Programming Approach to Integrate Gene Expression Data and Network Information for Pathway Model Generation. Bioinformatics. 36:169-176, 2020.
  3. Duolin Wang, Dongpeng Liu, Jiakang Yuchi, Fei He, Yuexu Jiang, Siteng Cai, Jingyi Li, Dong Xu. MusiteDeep: a deep-learning based webserver for protein post-translational modification site prediction and visualization. Nucleic Acids Research, 48:W140–W146, 2020.
  4. Ming Liu, Zhiqian Zhou, Penghui Shang, and Dong Xu. Fuzzified Image Enhancement for Deep Learning in Iris Recognition. IEEE Transactions on Fuzzy Systems. 28:92-99, 2020.
  5. Xiaoyue Feng, Hao Zhang, Yijie Ren, Penghui Shang, Yi Zhu, Yanchun Liang, Renchu Guan, and Dong Xu. Pubmender: Deep Learning Based Recommender System for Biomedical Publication Venue. Journal of Medical Internet Research. 21:e12957, 2019.
  6. Duolin Wang, Yanchun Liang, Dong Xu. Capsule Network for Protein Post-Translational Modification Site Prediction. Bioinformatics. 35: 2386–2394, 2019
  7. Mary Galli, Arjun Khakhar, Zefu Lu, Zongliang Chen, Sidharth Sen, Trupti Joshi, Jennifer L. Nemhauser, Robert J. Schmitz, and Andrea Gallavotti. The DNA binding landscape of the maize auxin response factor family. Nature Communications. Nature Communications. 2018. Volume 9, Article number: 4526 (2018).
  8. Majumder K, Wang J, Boftsi M, Fuller MS, Rede JE, Joshi T, Pintel DJ. Parvovirus minute virus of mice interacts with sites of cellular DNA damage to establish and amplify its lytic infection. eLife. 2018;7:e37750.
  9. Duolin Wang, Juexin Wang, Yuexu Jiang, Yanchun Liang, Dong Xu. BFDCA: A Comprehensive Tool of Using Bayes Factor for Differential Co-expression Analysis. Journal of Molecular Biology. 429:446–453, 2017.
  10. Duolin Wang, Shuai Zeng, Chunhui Xu, Wangren Qiu, Yanchun Liang, Trupti Joshi, Dong Xu. MusiteDeep: A Deep-learning Framework for General and Kinase-specific Phosphorylation Site Prediction. Bioinformatics. 33(24):3909-3916, 2017.