Graph convolutional networks for epigenetic state prediction using both sequence and 3D genome data
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Abstract
Motivation: Predictive models of DNA chromatin profile (i.e. epigenetic state), such as transcription factor binding, are essential for understanding regulatory processes and developing gene therapies. It is known that the 3D genome, or spatial structure of DNA, is highly influential in the chromatin profile. Deep neural networks have achieved state of the art performance on chromatin profile prediction by using short windows of DNA sequences independently. These methods, however, ignore the long-range dependencies when predicting the chromatin profiles because modeling the 3D genome is challenging. Results: In this work, we introduce ChromeGCN, a graph convolutional network for chromatin profile prediction by fusing both local sequence and long-range 3D genome information. By incorporating the 3D genome, we relax the independent and identically distributed assumption of local windows for a better representation of DNA. ChromeGCN explicitly incorporates known long-range interactions into the modeling, allowing us to identify and interpret those important long-range dependencies in influencing chromatin profiles. We show experimentally that by fusing sequential and 3D genome data using ChromeGCN, we get a significant improvement over the state-of-the-art deep learning methods as indicated by three metrics. Importantly, we show that ChromeGCN is particularly useful for identifying epigenetic effects in those DNA windows that have a high degree of interactions with other DNA windows. Availability and implementation: https://github.com/QData/ChromeGCN. Supplementary information: Supplementary data are available at Bioinformatics online.
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EHRs are often supplemented with other modalities such as diseases, symptoms, molecular data, drug interactions, etc.
“EHRs are often supplemented with other modalities, such as diseases, symptoms, molecular data, drug interactions, etc [7, 206, 211, 212].”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [206]
EHRs have underlying spatial and/or temporal dependencies.
“EHRs also have underlying spatial and/or temporal dependencies [206] that many methods have recently taken advantage of to perform time-dependent prediction tasks.”
From Graph representation learning in biomedicine and healthcare (2022) · cites this paper as [206]
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Sorted by citations
Graph representation learning in biomedicine and healthcare
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- Authors
- Lanchantin, J., & Qi, Y.
- Affiliations
- University of Virginia (US)
- Corresponding author
- Lanchantin, Jack
- Qi, Yanjun
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-12
- Volume
- 36
- Issue
- Supplement_2
- Pages
- i659-i667
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W3114860617
- Cited by
- 17
- References
- 37
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 2 · 2022: 7 · 2021: 1
- FWCI
- 0.81
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Bioinformatics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (1453580)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Epigenetics and DNA Methylation
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
- Keywords
- graph convolutional networks, 3D genome organization, transcription factor binding, long-range dependencies
- MeSH terms
- Chromatin, Neural Networks, Computer, Genome, Epigenesis, Genetic, Epigenomics
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