• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

H-DNA Areas Recognition Using Machine Learning Methods

Student: Maria Bochkareva

Supervisor: Maria Poptsova

Faculty: Faculty of Computer Science

Educational Programme: Applied Mathematics and Information Science (Bachelor)

Year of Graduation: 2020

A lot of discoveries have been made in genetic engineering in the last decades. The rapid growth in such areas as machine learning and bioinformatics prompted the corresponded growth in creating vast range of models which are able to find patterns in DNA structures and even to generate their different chains. However, considering the fact that CNN (convolutional neural network) models are the best in that area, the problem of selecting prevailed factors in model decision-making has become complicated. The main goal of this paper is to solve the H-DNA segments recognition problem in human genome using different ML methods. In particular, we will obtain the CNN model with the high recognition accuracy in order to define the significant features of chain parts. This model will respond why a chain part is exactly an H-DNA.

Student Theses at HSE must be completed in accordance with the University Rules and regulations specified by each educational programme.

Summaries of all theses must be published and made freely available on the HSE website.

The full text of a thesis can be published in open access on the HSE website only if the authoring student (copyright holder) agrees, or, if the thesis was written by a team of students, if all the co-authors (copyright holders) agree. After a thesis is published on the HSE website, it obtains the status of an online publication.

Student theses are objects of copyright and their use is subject to limitations in accordance with the Russian Federation’s law on intellectual property.

In the event that a thesis is quoted or otherwise used, reference to the author’s name and the source of quotation is required.

Search all student theses