Tampere University of Technology

TUTCRIS Research Portal

Predicting gene expression levels from histone modification signals with convolutional recurrent neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationEMBEC and NBC 2017 - Joint Conference of the European Medical and Biological Engineering Conference EMBEC 2017 and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2017
PublisherSpringer Verlag
Pages555-558
Number of pages4
ISBN (Print)9789811051210
DOIs
Publication statusPublished - 2018
Publication typeA4 Article in a conference publication
EventJoint Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC) -
Duration: 1 Jan 1900 → …

Publication series

NameIFMBE Proceedings
Volume65
ISSN (Print)1680-0737

Conference

ConferenceJoint Conference of the European Medical and Biological Engineering Conference (EMBEC) and the Nordic-Baltic Conference on Biomedical Engineering and Medical Physics (NBC)
Period1/01/00 → …

Abstract

In this paper we study how a Convolutional Recurrent Neural Network performs for predicting the gene expression levels from histone modification signals. Moreover, we consider two simplified variants of the Convolutional Recurrent Neural Network: Convolutional Neural Network and Recurrent Neural Network. The performance of the methods is evaluated with histone modification signal and gene expression data derived from Roadmap Epigenomics Mapping Consortium database, and compared against the state of the art method: the DeepChrome. It is shown that the proposed models give a statistically significant improvement over the baseline.

ASJC Scopus subject areas

Keywords

  • Convolutional neural networks, Convolutional recurrent neural networks, Gene expression, Histone modification, Recurrent neural networks

Publication forum classification

Field of science, Statistics Finland