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Cited by 142Explainable Artificial Intelligence (XAI)Machine Learning in BioinformaticsGenomics and Chromatin Dynamics

Interpretation of deep learning in genomics and epigenomics

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Talukder, Amlan, Barham, Clayton, Li, Xiaoman, Hu, Haiyan·2021·Briefings in Bioinformatics
DOI 10.1093/bib/bbaa177PMC PMC8138893
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Abstract

Machine learning methods have been widely applied to big data analysis in genomics and epigenomics research. Although accuracy and efficiency are common goals in many modeling tasks, model interpretability is especially important to these studies towards understanding the underlying molecular and cellular mechanisms. Deep neural networks (DNNs) have recently gained popularity in various types of genomic and epigenomic studies due to their capabilities in utilizing large-scale high-throughput bioinformatics data and achieving high accuracy in predictions and classifications. However, DNNs are often challenged by their potential to explain the predictions due to their black-box nature. In this review, we present current development in the model interpretation of DNNs, focusing on their applications in genomics and epigenomics. We first describe state-of-the-art DNN interpretation methods in representative machine learning fields. We then summarize the DNN interpretation methods in recent studies on genomics and epigenomics, focusing on current data- and computing-intensive topics such as sequence motif identification, genetic variations, gene expression, chromatin interactions and non-coding RNAs. We also present the biological discoveries that resulted from these interpretation methods. We finally discuss the advantages and limitations of current interpretation approaches in the context of genomic and epigenomic studies. Contact:xiaoman@mail.ucf.edu, haihu@cs.ucf.edu

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  • Borzoi enables direct characterization of tissue-specific cis-regulatory TF motifs by applying attribution methods to predicted RNA-seq coverage statistics.

    “Borzoi enables direct characterization of tissue-specific cis-regulatory TF motifs by applying attribution methods to the predicted RNA-seq coverage statistics45–50.”

    From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [45]

    The Chromatin interactions section describes saliency maps for Basenji, and the Gene expression prediction section discusses RNA-seq as a data source, but neither addresses Borzoi. Searches for Borzoi and for attribution of predicted RNA-seq coverage to tissue-specific TF motifs found no passage supporting the claim in our copy; page numbers were not available. (LLM, conf=0.76)

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Cited by

Sorted by citations

Authors
Talukder, A., Barham, C., Li, X., & Hu, H.
Affiliations
University of Central Florida (US)
Corresponding author
  • Li, Xiaoman
  • Hu, Haiyan
ORCID
Venue
Briefings in Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2020-08-20
Volume
22
Issue
3
ISSN
  • 1467-5463 (ISSN-L)
  • 1477-4054
DOI
10.1093/bib/bbaa177
PMC
PMC8138893
OpenAlex
W3081278968
Cited by
142
References
12
Citations by year
2026: 13 · 2025: 33 · 2024: 31 · 2023: 20 · 2022: 31 · 2021: 13 · 2020: 1
FWCI
0.56
Type
journal article
Language
en
Open access
Licence
Locations
  • Briefings in Bioinformatics (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
Funders
  • National Science Foundation (2015838, 1661414)
  • National Institute of Health (R15HGM123407)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords
deep neural network interpretability, model interpretability, genomics, epigenomics, black-box models, genetic variation, gene expression, chromatin interactions, non-coding RNA, explainable machine learning
MeSH terms
Deep Learning, Chromatin, DNA, Protein Binding, RNA, Gene Expression, Neural Networks, Computer, Computational Biology, Genomics, Epigenesis, Genetic

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