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Comparative Analysis of Deep Learning Methods for the Tasks of Functional Genomic Element Recognition

ФИО студента: Anastasiia Voronkova

Руководитель: Maria Poptsova

Кампус/факультет: Faculty of Computer Science

Программа: Software Engineering (Bachelor)

Год защиты: 2020

G-quadruplexes are parts of DNA with noncanonical forms. Nowadays their functions are not full studied yet, but it is has been shown, that they can be associated with various diseases, primarily - oncological or neurological diseases. So the study of quadruplexes can be quite useful for drug development. The aim of this paper is the study of deep learning methods for recognition of G-quadruplexes in DNA. In particular the usage of convolution and recurrent neural networks is considered as a basic approach for the task, and also recently invented architecture of - transformers. It was shown that they can learn complex dependencies and return good results not only on texts, but also on nucleotide sequences. he paper contains 29 pages, 4 chapters, 11 illustrations, 1 table, 24 bibliography items, 1 appendix. Keywords: G-quadruplexes, machine learning, neural networks, DNA, transformers.

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