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Recognition of Triplexes Using Omics Data with Deep Learning Methods

Student: Maria Fomenko

Supervisor: Maria Poptsova

Faculty: Faculty of Computer Science

Educational Programme: Data Analysis for Biology and Medicine (Master)

Year of Graduation: 2021

Many long non-coding RNAs are actively transcribed in a cell nucleus and some of them are known to play various regulatory roles in biological processes. One of the ways these lncRNAs interact with other molecules and complexes is the formation of RNA-DNA-DNA triplexes through hydrogen bonding with double-stranded DNA. In this work I have developed a deep learning model which predicts triplex-forming sites in the human genome using DNA sequence and information from omics data. I have also performed an interpretation of the model by identifying features that are considered to be the most important for prediction. Several features extracted from the model provide potentially useful insights into regulatory roles of triplexes.

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