Year of Graduation
Deep Neural Networks for Predicting the Functional Elements of the Genome
Applied Mathematics and Information Science
Regions of the left-handed form of Z-DNA were found in genomes of different species. There is an experimental evidence that Z-DNA plays a role in transcription, chromatin remodeling, and recombination. The association of epigenetic factors with Z-DNA sites remains poorly understood. The aim of this work is to determine the Z-DNA sites in the human genome associated with epigenetic markers with the help of machine learning (ML) models. The effectiveness of convolution, fully connected and recurrent neural networks (CNN, FC RNN) in comparison with base-line machine learning models is investigated. It was shown that convolution networks improve the efficiency of predictions but an addition of recurrent networks to convolution even more considerably increases the model performance. The results demonstrate the practical relevance of deep-learning methods for bioinformatics tasks.