About Ramzan Umarov Ramzan Umarov Ph.D. Student, Computer Science machine learning Deep learning 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. Research Interests The main research interest of Ramzan Umarov is applied machine learning especially Deep Learning. Professional Profile 2011-2012, Programmer, Softberry, Mount Kisko, NY, USA 2013-2014, Programmer Events Presented Events Feb 16 - Feb 22, 2020 Novel computational methods for promoter identification and analysis Ramzan Umarov, Ph.D. Student, Computer Science Feb 16, 16:00 - 18:00 B2 L5 R5209 machine learning promoters protein coding RNA genes TSS prediction tools In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method. Mar 3 - Mar 9, 2019 Promoters identification and analysis Ramzan Umarov, Ph.D. Student, Computer Science Mar 7, 16:00 - 18:00 B1 L2 R2202 promoters protein coding RNA genes TSS prediction tools Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.
Novel computational methods for promoter identification and analysis Ramzan Umarov, Ph.D. Student, Computer Science Feb 16, 16:00 - 18:00 B2 L5 R5209 machine learning promoters protein coding RNA genes TSS prediction tools In this dissertation, I present the methods I have developed for prediction of promoters for different organisms. Instead of focusing on the classification accuracy of the discrimination between promoter and non-promoter sequences, I predict the exact positions of the TSS inside the genomic sequences, testing every possible location. The developed methods significantly outperform the previous promoter prediction programs by considerably reducing the number of false positive predictions. Specifically, to reduce the false positive rate, the models are adaptively and iteratively trained by changing the distribution of samples in the training set based on the false positive errors made in the previous iteration. The new methods are used to gain insights into the design principles of the core promoters. Using model analysis, I have identified the most important core promoter elements and their effect on the promoter activity. I have developed a novel general approach to detect long range interactions in the input of a deep learning model, which was used to find related positions inside the promoter region. The final model was applied to the genomes of different species without a significant drop in the performance, demonstrating a high generality of the developed method.
Promoters identification and analysis Ramzan Umarov, Ph.D. Student, Computer Science Mar 7, 16:00 - 18:00 B1 L2 R2202 promoters protein coding RNA genes TSS prediction tools Promoter is a key region that is involved in differential transcription regulation of protein-coding and RNA genes. The gene-specific architecture of promoter sequences makes it extremely difficult to devise the general strategy for their computational identification.
Engage ORCID ShareClipboard Related Sites Structural and Functional Bioinformatics (SFB) Computer Science (CS) Related Content Articles 1 Events 2 Related Links Publications list on ORCID Structural and Functional Bioinformatics Group (SFB)
