My research lies in the interface of machine learning, combinatorial and numerical optimization and some data-rich areas such as computational biology. I am interested in studying theoretically sound and empirically efficient machine learning and optimization algorithms to analyze, interpret, and integrate high volumes of multi-dimensional and heterogeneous data and based upon which building effective predictive models. On one hand, I would like to develop computational methods for addressing a specific challenging problem by taking full advantage of all available data and domain knowledge. On the other hand, inspired by real-world applications I would like to study novel machine learning and optimization algorithms that is widely applicable.
1. Invited to give a keynote talk at ISMB 3DSIG 2019.
2. One 12-year old paper entitled “Pairwise Global Alignment of Protein Interaction Networks by Matching Neighborhood Topology” received the Test-of-Time Award at RECOMB 2019. See here for news.
3. Our deep learning method for protein contact prediction and tertiary structure prediction is ranked 1st and 2nd (in server category), respectively, in CASP13 (the 13th Critical Assessment of Protein Structure Prediction). All the top predictors in contact prediction and template-free modeling adopted the deep learning algorithm we first published in “Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model”.
4. Our deep learning paper for contact prediction entitled “Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model” won the 2018 PLoS Computational Biology Research Prize in the category of Breakthrough/Innovation.
5. Invited to give a keynote talk at IEEE ICCABS on Oct 19, 2017.
6. Our deep learning method for protein contact prediction is ranked 1st in CASP12, the 12th Critical Assessment of Protein Structure Prediction. See here for a ranking list and our paper for technical details. This work is also covered by The Economist.
7. Science reports our protein structure prediction server RaptorX. See the report for more details.
8. Our paper entitled "CNVnet: Combining Sparse Learning and Biological Networks to Capture Joint Effect of Copy Number Variants" has received the ACM SIGBio Best Student Paper Award during the ACM-BCB 2014. The authors are Mr. Zhiyong Wang (PhD student), Prof. Xinghua Shi (UNCC) and Jinbo Xu.
9. The first three PhD students in my group have landed their desirable jobs: Dr. Jian Peng (tenure-track faculty at UIUC CS), Dr. Feng Zhao (finance industry in New York), and Zhiyong Wang (Google).
10. Our presentation entitled MRFalign: protein remote homology detection through alignment of Markov Random Fields won the Warren DeLano Award for Structural Bioinformatics and Computational Biophysics in ISMB 3DSIG 2014. ISMB is one of the top 2 bioinformatics conferences.
11. Our paper entitled MRFalign: protein remote homology detection through alignment of Markov Random Fields won the best paper award in RECOMB 2014, one of the top 2 bioinformatics conferences.
12. Our PhD student Mr. Jianzhu Ma won the best poster award in the 2013 Zing conference for protein and RNA structure analysis. The poster is about protein homology detection by aligning two Markov Random Fields.
13. Our protein structure prediction program RaptorX was ranked among top 10 for the CASP10 hardest template-based modeling targets. RaptorX is the only server among the top 10 groups. The other 9 are human groups, which can make use of server results.
14. In ISMB 3DSIG 2012, Dr. Sheng Wang won the Warren DeLano Award for Structural Bioinformatics and Computational Biophysics for his work entitled protein structure alignment beyond spatial proximity, which is implemented as DeepAlign (available at http://ttic.uchicago.edu/~jinbo/software.htm).
15. Our protein structure prediction program RaptorX was ranked No.1 among ~80 CASP9 participating servers for the 50 hardest TBM (template-based modeling) targets in CASP9 and voted as one of the most interesting and innovative methods by the CASP9 community. Our team was invited to give three talks at the CASP9 meeting.
16. Our PhD student Mr. Jian Peng wins the prestigious Microsoft Research PhD Fellowship 2010. Only 10 out of 176 applications received this award in 2010.
17. Our poster entitled Boosting Protein Threading Accuracy won the Best Poster Award in RECOMB 2009, one of the best conferences in the field of Computational Biology and Bioinformatics.
18. The following
poster won the Best Poster Award in CASP8
conference held in
J. DeBartolo, G. Hockey, F. Zhao, J. Peng, A. Augustyn, A. Adhikar, J. Xu, K. F. Freed and T. R. Sosnick. Structure prediction combining the template-based RAPTOR algorithm with the ItFix ab initio method.
16. Our protein structure prediction program RAPTOR ranked No.2-No.4 among all automated programs in CASP8, according to several assessments.
1. Jinbo Xu. NSF/CCF AF-1618648. AF:III: small: Convex optimization for protein-protein interaction network alignment. Total cost $300k, 7/1/2016-6/30/2019.
2. Jinbo Xu. NSF/BIO-1564955. ABI Development: Developing RaptorX Web Portal for Protein Structure and Functional Study. Total cost $557k, 7/15/2016-6/30/2019.
3. Jinbo Xu. NIH R01GM089753. New computational methods for data-driven protein structure prediction. Total cost ~$2.65m, 5/1/2010-8/31/2019
4. Jinbo Xu. NSF/CCF AF-1149811 (CAREER award). Exact and approximate algorithms for 3D structure modeling of protein-protein interactions. Total cost $500k, 7/1/2012-6/30/2017
5. Jinbo Xu. NSF/BIO DBI-1262603. Continued development of RaptorX for protein structure and function prediction. Total cost ~$540k, 7/1/2013-6/30/2016
6. Jinbo Xu. NSF/BIO DBI-0960390: Algorithm and web server for low-homology protein threading. Total cost ~$408k, 7/1/2010-6/30/2013
7. Tobin Sosnick (PI), Karl Freed, and Jinbo Xu. NIH R01GM081642. Protein structure refinement using novel move set. My share ~$200k, 08/2007-08/2010
8. Bonnie Berger (PI), Jinbo Xu and Jadwiga Bienkowska. NIH R01GM081871A1. Prediction of protein interactome. My share ~$100k, 12/2007-12/2012
9. Bonnie Berger (PI), Jinbo Xu and Jadwiga Bienkowska. NIH R01GM081871A1. Prediction of protein interactome. My share ~$100k, 12/2007-12/2012