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Machine Learning Approach to Reveal Functional Elements in Human Genome

Student: Timur Iakobidze

Supervisor: Maria Poptsova

Faculty: Graduate School of Business

Educational Programme: Big Data Systems (Master)

Year of Graduation: 2018

Bioinformatics becomes an excellent source of big data. Knowing a person’s individual genomic features, doctors can find optimal ways to healing person’s diseases. Such approach is called personalized medicine. We will explore stem-loop secondary structures, which by an opinion of some scientists, could modulate binding of transcription factors (TF) with the goal to annotate stem-loop structures associated with TF as novel genomic functional elements. This research is trying to prove the existence of the relationship between stem- loop distribution density and the density of TF. We built an automated parallelized data processing pipeline. We downloaded and analysed about 10 000 samples of the TF binding sites data from the ENCODE project web-page. The performed analysis has proved the hypothesis. Results have a great potential to be integrated into the programming packages in the sphere of personalised medicine. It has become already a common knowledge that mutations associated with a certain disease are patient specific, and that is why the real-time comparison of a novel patient data with the existing data is of great importance.

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