Recurrent Deep Neural Networks for Nucleosome Classification

Giosue' Lo Bosco, Domenico Amato, Mattia Antonino Di Gangi, Riccardo Rizzo, Mattia Antonino Di Gangi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Nucleosomes are the fundamental repeating unit of chromatin. A nucleosome is an 8 histone proteins complex, in which approximately 147–150 pairs of DNA bases bind. Several biological studies have clearly stated that the regulation of cell type-specific gene activities are influenced by nucleosome positioning. Bioinformatic studies have improved those results showing proof of sequence specificity in nucleosomes’ DNA fragment. In this work, we present a recurrent neural network that uses nucleosome sequence features representation for their classification. In particular, we implement an architecture which stacks convolutional and long short-term memory layers, with the main purpose to avoid the features extraction and selection steps. We have computed classifications using eight datasets of three different organisms with a growing genome complexity, from yeast to human. We have also studied the capability of the model trained on the highest complex species in recognizing nucleosomes of the other organisms.
Original languageEnglish
Title of host publicationComputational Intelligence Methods for Bioinformatics and Biostatistics, 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, 2018
Pages118-127
Number of pages10
Publication statusPublished - 2020

Publication series

NameLECTURE NOTES IN COMPUTER SCIENCE

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science

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