Prof. Yanjun Qi, PhD @ UVA (email: yanjun at   

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Basic Information

Award title: CAREER: A Data-Driven Network Inference Framework for Context-Conditioned Protein Interaction Graphs
NSF CAREER Abstract Page @ National Science Foundation (NSF)
Acknowledgement: This material is based upon work supported by the National Science Foundation under Grant No. 1453580.
Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
Duration (expected): 2015-2020
Role: Dr. Yanjun Qi, Principal Investigator
Point of Contact: PI's email (
Student(s): PhD candidates : Beilun Wang and Ritambhara Singh


Research Challenges: Modern multi-context molecular datasets are high dimensional, heterogeneous and noisy. This proposal aims to design novel and robust machine-learning algorithms to identify context-specific interaction graphs from such data. Helping researchers effectively translate aggregated data into knowledge that take the form of graphs, this project can have important biomedical applications, such as investigating molecular signatures corresponding to different drug treatments. It is expected to impact other domains as well, for instance, to identify condition-specific functional networks about human brain connectivity.

Current Results (2015-2018)

Publication: Beilun Wang, Arshdeep Sekhon, Yanjun Qi, (2018)
"A fast and scalable joint estimator for integrating additional knowledge in learning multiple related sparse gaussian graphical models.", Proceedings of The 35th International Conference on Machine Learning (ICML)

(Link) (Talk) (GitHub)

Publication: Beilun Wang, Arshdeep Sekhon, Yanjun Qi, (2018)
"Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure", Proceedings of the 21th International Conference on Artificial Intelligence and Statistics (AISTATS)

(Arxiv) (Poster) (Talk) (GitHub) (Rtool)

Publication: Beilun Wang, Ji Gao, Yanjun Qi, (2017) "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models", Proceedings of The 20th International Conference on Artificial Intelligence and Statistics (AISTATS);

(Arxiv) (PDF)
(GitURL) (Rpage)

Publications: Beilun Wang, Ritambhara Singh, Yanjun Qi, (2017) "A constrained L1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models", to appear at Journal of (Machine Learning) (also @ the International Conference on Machine Learning 2016, (Computational Biology Workshop );

(Arxiv) (PDF)
(Rpage) (GitURL)

Publications: Ritambhara Singh, Jack Lanchantin, Gabriel Robins, Yanjun Qi, (2016) "Transfer String Kernel for Cross-Context DNA-Protein Binding Prediction", @ IEEE/ACM Transactions on Computational Biology and Bioinformatics (Journal), (TCBB) ;

(Arxiv) (online)

Publications: Sarah Mohamed, Nick Janus, Yanjun Qi, (2016) "SCODE: A Cytoscape app for supervised complex detection in protein-protein interaction graphs", (F1000Research );

Educational material: Over the school year15-16, we have one undergraduate research assistant through UVA undergraduate capstone project to perform data-driven network learning research ;
Broader Impacts: The proposed research is expected to impact other domains as well, for instance, social-network discovery and condition-specific network inference for brain connectivity.
Project Website:
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