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Student
Title
Supervisor
Faculty
Educational Programme
Final Grade
Year of Graduation
Timur Iakobidze
Machine Learning Approach to Reveal Functional Elements in Human Genome
Big Data Systems
(Master’s programme)
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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