- BSc, Computer Science, Royal Holloway University of London, 2012.
- MSc, Advanced Computing, Imperial College London, 2013.
- Ph.D., Computer Science, KAUST, Thuwal, Saudi Arabia, 2014.
Honors & Awards
Following awards were received at Royal Holloway.
- The Lilian F Heather Prize – This is given to students that carried out excellent work in the year 1.
- Annual Driver Prize – This is given to the best first year student.
- Martin-Holloway prize – This is given to best single honours finalist in each faculty.
Received at KAUST:
- CEMSE Dean Award
Ramzan is a PhD candidate, working on solving biological problems by using applied machine learning with focus on deep learning. He has developed deep learning based methods to solve bioinformatics problems achieving state-of-the-art performance, focusing on various aspects of gene regulation. Umarov obtained his Master degree from Imperial College London, Advanced Computing course.
The main research interest of Ramzan Umarov is applied machine learning especially Deep Learning.
- 2011-2012, Programmer, Softberry, Mount Kisko, NY, USA
- 2013-2014, Programmer, Chechen State University, Grozny, Russia
Umarov, R. K., & Solovyev, V. V. (2017). Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks. PLOS ONE, 12(2), e0171410. doi:10.1371/journal.pone.0171410
Shahmuradov, I. A., Umarov, R. K., & Solovyev, V. V. (2017). TSSPlant: a new tool for prediction of plant Pol II promoters. Nucleic Acids Research, gkw1353. doi:10.1093/nar/gkw1353
Dai, H., Umarov, R., Kuwahara, H., Li, Y., Song, L., & Gao, X. (2017). Sequence2Vec: a novel embedding approach for modeling transcription factor binding affinity landscape. Bioinformatics, 33(22), 3575–3583. doi:10.1093/bioinformatics/btx480
Li, Y., Wang, S., Umarov, R., Xie, B., Fan, M., Li, L., & Gao, X. (2017). DEEPre: sequence-based enzyme EC number prediction by deep learning. Bioinformatics, 34(5), 760–769. doi:10.1093/bioinformatics/btx680
Kuwahara, H., Umarov, R., Almasri, I., & Gao, X. (2017). ACRE: Absolute concentration robustness exploration in module-based combinatorial networks. Synthetic Biology, 2(1). doi:10.1093/synbio/ysx001