Farhan Quadir

About Me

I am a Computer Science PhD student at the Electrical Engineering and Computer Science (EECS) Department, University of Missouri-Columbia. I work in the Bioinformatics, and Machine Learning Lab under the supervision of Dr. Jianlin Cheng. I have been a student here since Fall 2018.

Research Interests

I am mainly interested in protein structure prediction especially of protein complexes. My job is to create machine learning (deep learning) algorithms that can accurately predict intrachain and interchain protein distances, contacts, etc. and use these results to build accurate models of tertiary and quaternary structures. I am also interested in new features related to proteins and reinforcement learning.

Research Profile

1. Google Scholar: https://scholar.google.com/citations?user=0L8DEpEAAAAJ&hl=en

2. ResearchGate: https://www.researchgate.net/profile/Farhan-Quadir

3. ORCID Profile: https://orcid.org/0000-0003-0480-5714

Research Projects Websites

1. DeepComplex:

Publications

1. Hou, J., Wu, T., Guo, Z., Quadir, F., & Cheng, J. (2020). The MULTICOM Protein Structure Prediction Server Empowered by Deep Learning and Contact Distance Prediction. In Protein Structure Prediction (pp. 13-26). Humana, New York, NY. https://link.springer.com/protocol/10.1007/978-1-0716-0708-4_2

2. Quadir, F., Roy, R. S., Halfmann, R., & Cheng, J. (2021). DNCON2_Inter: predicting interchain contacts for homodimeric and homomultimeric protein complexes using multiple sequence alignments of monomers and deep learning. Scientific reports, 11(1), 1-10. https://doi.org/10.1038/s41598-021-91827-7

3. Quadir, F., Al Ameen, M. F., & Momen, S. (2014, December). Visualization and queuing analysis of spatio-temporal traffic data. In 2014 17th International Conference on Computer and Information Technology (ICCIT) (pp. 223-228). IEEE. https://ieeexplore.ieee.org/abstract/document/7073106

4. Soltanikazemi, E., Quadir, F., Roy, R. S., & Cheng, J. (2021). Distance-based Reconstruction of Protein Quaternary Structures from Inter-Chain Contacts. Proteins: Structure, Function and Bioinformatics. pp: 1-12. doi: https://doi.org/10.1002/prot.26269.

5. Quadir, F., Roy, R. S., Soltanikazemi, E., & Cheng, J. (2021). DeepComplex: A Web Server of Predicting Protein Complex Structures by Deep Learning Inter-Chain Contact Prediction and Distance-Based Modelling. Frontiers in Molecular Biosciences, 8:716973. doi: https://doi.org/10.3389/fmolb.2021.716973

6. Raj S. R., Quadir, F., Soltanikazemi, E., & Cheng, J. (2022). A deep dilated convolutional residual network for predicting interchain contacts of protein homodimers. Bioinformatics. Oxford University Press. doi: https://doi.org/10.1093/bioinformatics/btac063.

7. M. Gao., et al., High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function. 2021 IEEE/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), pp. 46-57, doi: https://doi.org/10.1109/MLHPC54614.2021.00010.

8. Lensink, M. H., et al., (2021) Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment. Proteins: Structure, Function and Bioinformatics. Volume 89, Issue 12, pp. 1800-1823. doi: https://doi.org/10.1002/prot.26222