Integrative computational epigenomics to build data-driven gene regulation hypotheses

In this review, we perform a critical analysis of methods with the explicit aim of harmonising data, as opposed to case-specific integration. This revealed that matrix factorisation, latent variable analysis, and deep learning are potent strategies. Finally, we describe the properties of an ideal universal data harmonisation framework.

Online version

Download paper here

Plain text citation:

Tyrone Chen, Sonika Tyagi, "Integrative computational epigenomics to build data-driven gene regulation hypotheses." GigaScience, Volume 9, Issue 6, June 2020, giaa064. DOI: https://doi.org/10.1093/gigascience/giaa064

Bibtex citation:

@article{10.1093/gigascience/giaa064,
    author = {Chen, Tyrone and Tyagi, Sonika},
    title = "{Integrative computational epigenomics to build data-driven gene regulation hypotheses}",
    journal = {GigaScience},
    volume = {9},
    number = {6},
    year = {2020},
    month = {06},
    issn = {2047-217X},
    doi = {10.1093/gigascience/giaa064},
    url = {https://doi.org/10.1093/gigascience/giaa064},
    note = {giaa064},
    eprint = {https://academic.oup.com/gigascience/article-pdf/9/6/giaa064/33393761/giaa064.pdf},
}