Abstract
Artificial intelligence (AI) models based on deep learning now represent the state of the art for making functional predictions in genomics research. However, the underlying basis on which predictive models make such predictions is often unknown. For genomics researchers, this missing explanatory information would frequently be of greater value than the predictions themselves, as it can enable new insights into genetic processes. We review progress in the emerging area of explainable AI (xAI), a field with the potential to empower life science researchers to gain mechanistic insights into complex deep learning models. We discuss and categorize approaches for model interpretation, including an intuitive understanding of how each approach works and their underlying assumptions and limitations in the context of typical high-throughput biological datasets.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$189.00 per year
only $15.75 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Angermueller, C., Pärnamaa, T., Parts, L. & Stegle, O. Deep learning for computational biology. Mol. Syst. Biol. 12, 878 (2016).
Zou, J. et al. A primer on deep learning in genomics. Nat. Genet. 51, 12–18 (2019).
Ching, T. et al. Opportunities and obstacles for deep learning in biology and medicine. J. R. Soc. Interface 15, 20170387 (2018).
Eraslan, G., Avsec, Ž., Gagneur, J. & Theis, F. J. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20, 389–403 (2019). This review paper provides a succinct overview of deep learning in genomics, suitable for biomedical researchers.
Molnar, C., Casalicchio, G. & Bischl, B. Interpretable machine learning – a brief history, state-of-the-art and challenges. Preprint at arXiv https://doi.org/10.48550/arXiv.2010.09337 (2020). This textbook provides an overview of approaches for interpreting machine learning models.
Toneyan, S., Tang, Z. & Koo, P. K. Evaluating deep learning for predicting epigenomic profiles. Preprint at bioRxiv https://doi.org/10.1101/2022.04.29.490059 (2022).
Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015). One of the first papers to use a sequence-to-activity neural network for a broad class of regulatory genomics tasks.
Kelley, D. R., Snoek, J. & Rinn, J. L. Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks. Genome Res. 26, 990–999 (2016). One of the first papers to use a sequence-to-activity neural network for a broad class of regulatory genomics tasks.
Kim, D. S. et al. The dynamic, combinatorial cis-regulatory lexicon of epidermal differentiation. Nat. Genet. 53, 1564–1576 (2021).
Avsec, Ž. et al. Base-resolution models of transcription-factor binding reveal soft motif syntax. Nat. Genet. 53, 354–366 (2021). A pioneering paper that shows how non-linear relationship between motifs and context-dependent spacing can be derived using various post-hoc model interpretation techniques.
Maslova, A. et al. Deep learning of immune cell differentiation. Proc. Natl Acad. Sci. USA 117, 25655–25666 (2020).
Quang, D. & Xie, X. DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences. Nucleic Acids Res. 44, e107 (2016). A paper that proposes one of the first hybrid CNN–RNN models in genomics applications.
Alipanahi, B., Delong, A., Weirauch, M. T. & Frey, B. J. Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning. Nat. Biotechnol. 33, 831–838 (2015). This study introduces the application of CNNs to genomics.
Zhou, J. et al. Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk. Nat. Genet. 50, 1171–1179 (2018).
Kelley, D. R. et al. Sequential regulatory activity prediction across chromosomes with convolutional neural networks. Genome Res. 28, 739–750 (2018).
Avsec, Ž. et al. Effective gene expression prediction from sequence by integrating long-range interactions. Nat. Methods 18, 1196–1203 (2021). A first paper that introduces transformers and attention mechanism for improved prediction of gene expression from large input sequences.
Tasaki, S., Gaiteri, C., Mostafavi, S. & Wang, Y. Deep learning decodes the principles of differential gene expression. Nat. Mach. Intell. 2, 376–386 (2020).
Xiong, H. Y. et al. RNA splicing. The human splicing code reveals new insights into the genetic determinants of disease. Science 347, 1254806 (2015).
Leung, M. K. K., Xiong, H. Y., Lee, L. J. & Frey, B. J. Deep learning of the tissue-regulated splicing code. Bioinformatics 30, i121–i129 (2014).
Fudenberg, G., Kelley, D. R. & Pollard, K. S. Predicting 3D genome folding from DNA sequence with Akita. Nat. Methods 17, 1111–1117 (2020).
Lanchantin, J., Singh, R., Wang, B. & Qi, Y. Deep motif dashboard: visualizing and understanding genomic sequences using deep neural networks. Preprint at arXiv https://doi.org/10.48550/arXiv.1608.03644 (2016).
Covert, I., Lundberg, S. & Lee, S.-I. Explaining by removing: a unified framework for model explanation. J. Mach. Learn. Res. 22, 1–90 (2021). This paper presents a unified framework for understanding feature attribution methods.
Sundararajan, M., Taly, A. & Yan, Q. Axiomatic attribution for deep networks. Preprint at arXiv https://doi.org/10.48550/arXiv.1703.01365 (2017).
Ivanovs, M., Kadikis, R. & Ozols, K. Perturbation-based methods for explaining deep neural networks: a survey. Pattern Recognit. Lett. 150, 228–234 (2021).
Rozemberczki, B. et al. The Shapley value in machine learning. in Proc. 31st Int. Jt Conf. Artificial Intelligence (ed. De Raedt, L.) 5572–5579 (IJCAI, 2022).
Lundberg, S. M. & Lee, S.-I. A unified approach to interpreting model predictions. In Proc. 31st Int. Conf. Neural Information Processing Systems (eds von Luxburg, U. et al.) vol. 30 4768–4777 (NIPS, 2017). This paper presents a unified framework for interpretation and presents DeepSHAP.
LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).
Bau, D. et al. Understanding the role of individual units in a deep neural network. Proc. Natl Acad. Sci. USA 117, 30071–30078 (2020).
Luo, X., Tu, X., Ding, Y., Gao, G. & Deng, M. Expectation pooling: an effective and interpretable pooling method for predicting DNA-protein binding. Bioinformatics 36, 1405–1412 (2020).
Cuperus, J. et al. Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences. Preprint at bioRxiv https://doi.org/10.1101/137547 (2017).
Min, X. et al. Predicting enhancers with deep convolutional neural networks. BMC Bioinform. 18 (Suppl. 13), 478 (2017).
Castro-Mondragon, J. A. et al. JASPAR 2022: the 9th release of the open-access database of transcription factor binding profiles. Nucleic Acids Res. 50, D165–D173 (2022).
Weirauch, M. T. et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158, 1431–1443 (2014).
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I. & Salakhutdinov, R. R. Improving neural networks by preventing co-adaptation of feature detectors. Preprint at arXiv https://doi.org/10.48550/arXiv.1207.0580 (2012).
Goodfellow, I., Bengio, Y. & Courville, A. Deep Learning (MIT Press, 2016). A machine learning textbook that focuses on DNN models.
Koo, P. K. & Ploenzke, M. Improving representations of genomic sequence motifs in convolutional networks with exponential activations. Nat. Mach. Intell. 3, 258–266 (2021).
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67, 301–320 (2005).
Min, S., Lee, B. & Yoon, S. Deep learning in bioinformatics. Brief. Bioinform. 18, 851–869 (2017).
Chaudhari, S., Mithal, V., Polatkan, G. & Ramanath, R. An attentive survey of attention models. ACM Trans. Intell. Syst. Technol. 12, 1–32 (2021).
Vaswani, A. et al. Attention is all you need. in Proc. 31st Int. Conf. Neural Information Processing Systems (eds von Luxburg, U., Guyon, I., Bengio, S., Wallach, H. & Fergus, R.) vol. 30 5998-6008 (NIPS, 2017).
Bahdanau, D., Cho, K. & Bengio, Y. Neural machine translation by jointly learning to align and translate. Preprint at arXiv https://doi.org/10.48550/arXiv.1409.0473 (2014).
Park, S. et al. Enhancing the interpretability of transcription factor binding site prediction using attention mechanism. Sci. Rep. 10, 13413 (2020).
Mao, W., Kostka, D. & Chikina, M. Modeling enhancer–promoter interactions with attention-based neural networks. Preprint at bioRxiv https://doi.org/10.1101/219667 (2017).
Serrano, S. & Smith, N. A. Is attention interpretable? In Proc. 57th Annual Meeting of the Association for Computational Linguistics (eds Korhonen, A et al.) 2931–2951 (Association for Computational Linguistics, 2019).
Samek, W., Binder, A., Montavon, G., Bach, S. & Müller, K.-R. Evaluating the visualization of what a deep neural network has learned. IEEE Trans. Neural Netw. Learn. Syst. 28, 2660–2673 (2017).
Simonyan, K., Vedaldi, A. & Zisserman, A. Deep inside convolutional networks: visualising image classification models and saliency maps. Preprint at arXiv https://doi.org/10.48550/arXiv.1312.6034 (2013).
Zheng, A. et al. Deep neural networks identify sequence context features predictive of transcription factor binding. Nat. Mach. Intell. 3, 172–180 (2021).
Cochran, K. et al. Domain-adaptive neural networks improve cross-species prediction of transcription factor binding. Genome Res. 32, 512–523 (2022).
Nair, S., Shrikumar, A. & Kundaje, A. fastISM: performant in-silico saturation mutagenesis for convolutional neural networks. Preprint at bioRxiv https://doi.org/10.1101/2020.10.13.337147 (2020).
Schreiber, J., Nair, S., Balsubramani, A. & Kundaje, A. Accelerating in-silico saturation mutagenesis using compressed sensing. Preprint at bioRxiv https://doi.org/10.1101/2021.11.08.467498 (2021).
Washburn, J. D. et al. Evolutionarily informed deep learning methods for predicting relative transcript abundance from DNA sequence. Proc. Natl Acad. Sci. USA 116, 5542–5549 (2019).
Yuan, H. & Kelley, D. R. scBasset: sequence-based modeling of single cell ATAC-seq using convolutional neural networks. Preprint at bioRxiv https://doi.org/10.1101/2021.09.08.459495 (2021).
Greenside, P., Shimko, T., Fordyce, P. & Kundaje, A. Discovering epistatic feature interactions from neural network models of regulatory DNA sequences. Bioinformatics 34, i629–i637 (2018). A first paper describing how occlusion can be used to detect significant motif–motif epistasis.
de Almeida, B. P., Reiter, F., Pagani, M. & Stark, A. DeepSTARR predicts enhancer activity from DNA sequence and enables the de novo design of synthetic enhancers. Nat. Genet. 54, 613–624 (2022).
Prakash, E. I., Shrikumar, A. & Kundaje, A. Towards more realistic simulated datasets for benchmarking deep learning models in regulatory genomics. In Proc.16th Machine Learning in Computational Biology meeting (eds Knowles, D. A. et al.) vol. 165, 58–77 (PMLR, 2022).
Finnegan, A. & Song, J. S. Maximum entropy methods for extracting the learned features of deep neural networks. PLoS Comput. Biol. 13, e1005836 (2017).
Selvaraju, R. R. et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput Vis. 128, 336–359 (2020).
Sundararajan, M., Taly, A. & Yan, Q. Gradients of counterfactuals. Preprint at arXiv https://doi.org/10.48550/arXiv.1611.02639 (2016).
Huang, D. et al. Weakly supervised learning of RNA modifications from low-resolution epitranscriptome data. Bioinformatics 37, i222–i230 (2021).
Shrikumar, A., Greenside, P. & Kundaje, A. Learning important features through propagating activation differences. In Proc. 34th International Conference on Machine Learning (eds Precup, D. & Teh, Y. W.) vol. 70, 3145–3153 (PMLR, 2017). A technical paper that describes the DeepLIFT feature attribution method, one of the most widely used propagation-based methods in genomics.
Jha, A., K Aicher, J., Gazzara, M. R., Singh, D. & Barash, Y. Enhanced Integrated Gradients: improving interpretability of deep learning models using splicing codes as a case study. Genome Biol. 21, 149 (2020).
Jethani, N., Sudarshan, M., Covert, I., Lee, S.-I. & Ranganath, R. FastSHAP: real-time Shapley value estimation. Preprint at arXiv https://doi.org/10.48550/arXiv.2107.07436 (2021).
Shrikumar, A. et al. Technical note on transcription factor motif discovery from importance scores (TF-MoDISco) version 0.5.6.5. Preprint at arXiv https://doi.org/10.48550/arXiv.1811.00416 (2018).
Sahu, B. et al. Sequence determinants of human gene regulatory elements. Nat. Genet. 54, 283–294 (2022).
Koo, P. K., Majdandzic, A., Ploenzke, M., Anand, P. & Paul, S. B. Global importance analysis: An interpretability method to quantify importance of genomic features in deep neural networks. PLoS Comput. Biol. 17, e1008925 (2021).
Hammelman, J. & Gifford, D. K. Discovering differential genome sequence activity with interpretable and efficient deep learning. PLoS Comput. Biol. 17, e1009282 (2021).
Bogard, N., Linder, J., Rosenberg, A. B. & Seelig, G. A deep neural network for predicting and engineering alternative polyadenylation. Cell 178, 91–106.e23 (2019).
Yosinski, J., Clune, J., Nguyen, A., Fuchs, T. & Lipson, H. Understanding neural networks through deep visualization. Preprint at arXiv https://doi.org/10.48550/arXiv.1506.06579 (2015).
Brown, T. et al. Language models are few-shot learners. Adv. Neural Inf. Process. Syst. 33, 1877–1901 (2020).
Tao, Y. et al. Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers. Preprint at bioRxiv https://doi.org/10.1101/2021.09.07.459263 (2021).
Karbalayghareh, A., Sahin, M. & Leslie, C. S. Chromatin interaction-aware gene regulatory modeling with graph attention networks. Genome Res. 32, 930–944 (2022).
Ullah, F. & Ben-Hur, A. A self-attention model for inferring cooperativity between regulatory features. Nucleic Acids Res. 49, e77 (2021).
Ji, Y., Zhou, Z., Liu, H. & Davuluri, R. V. DNABERT: pre-trained bidirectional encoder representations from transformers model for DNA-language in genome. Bioinformatics 37, 2112–2120 (2021).
Janizek, J. D., Sturmfels, P. & Lee, S.-I. Explaining explanations: axiomatic feature interactions for deep networks. Preprint at arXiv https://doi.org/10.48550/arXiv.2002.04138 (2020).
Dombrowski, A.-K. et al. Explanations can be manipulated and geometry is to blame. Adv. Neural Inf. Process. Syst. 32, 13567–13578 (2019).
Ma, J. et al. Using deep learning to model the hierarchical structure and function of a cell. Nat. Methods 15, 290–298 (2018). This paper presents one of the first ‘transparent neural network’ models in genomics.
The Gene Ontology Consortium. Gene Ontology: tool for the unification of biology. Nat. Genet. 25, 25–29 (2000).
Fortelny, N. & Bock, C. Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data. Genome Biol. 21, 190 (2020).
Elmarakeby, H. A. et al. Biologically informed deep neural network for prostate cancer discovery. Nature 598, 348–352 (2021).
Tareen, A. & Kinney, J. B. Biophysical models of cis-regulation as interpretable neural networks. Preprint at arXiv https://doi.org/10.48550/arXiv.2001.03560 (2019).
Liu, Y., Barr, K. & Reinitz, J. Fully interpretable deep learning model of transcriptional control. Bioinformatics 36, i499–i507 (2020).
Agarwal, R. et al. Neural additive models: interpretable machine learning with neural nets. Preprint at arXiv https://doi.org/10.48550/arXiv.2004.13912 (2020).
Novakovsky, G., Fornes, O., Saraswat, M., Mostafavi, S. & Wasserman, W. W. ExplaiNN: interpretable and transparent neural networks for genomics. Preprint at bioRxiv https://doi.org/10.1101/2022.05.20.492818 (2022).
DeGrave, A. J., Janizek, J. D. & Lee, S.-I. AI for radiographic COVID-19 detection selects shortcuts over signal. Nat. Mach. Intell. 3, 610–619 (2021).
Heil, B. J. et al. Reproducibility standards for machine learning in the life sciences. Nat. Methods 18, 1132–1135 (2021).
Haibe-Kains, B. et al. Transparency and reproducibility in artificial intelligence. Nature 586, E14–E16 (2020).
Leman, D. V., Parshikov, A. F., Georgiev, P. G. & Maksimenko, O. G. Organization of the Drosophila melanogaster SF1 insulator and its role in transcription regulation in transgenic lines. Russ. J. Genet. 50, 341–347 (2014).
Lambert, S. A. et al. The human transcription factors. Cell 172, 650–665 (2018).
Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).
Carter, B. & Zhao, K. The epigenetic basis of cellular heterogeneity. Nat. Rev. Genet. 22, 235–250 (2021).
Rowley, M. J. & Corces, V. G. Organizational principles of 3D genome architecture. Nat. Rev. Genet. 19, 789–800 (2018).
Stormo, G. D. & Zhao, Y. Determining the specificity of protein-DNA interactions. Nat. Rev. Genet. 11, 751–760 (2010).
Xu, C. & Jackson, S. A. Machine learning and complex biological data. Genome Biol. 20, 76 (2019).
Koo, P. K. & Ploenzke, M. Deep learning for inferring transcription factor binding sites. Curr. Opin. Syst. Biol. 19, 16–23 (2020).
Whalen, S., Schreiber, J., Noble, W. S. & Pollard, K. S. Navigating the pitfalls of applying machine learning in genomics. Nat. Rev. Genet. 23, 169–181 (2022).
Acknowledgements
N.D. acknowledges the support of the Pacific Institute for the Mathematical Sciences (PIMS) Postdoctoral Fellowship program. W.W.W. acknowledges support from Natural Sciences and Engineering Research Council of Canada (NSERC) and the British Columbia (BC) Children’s Hospital Foundation. S.M. acknowledges support from the Canadian Institute for Advanced Research (CIFAR). M.W.L. acknowledges support from Genome Canada, Genome BC, NSERC and Health Research BC. The authors thank W. Stafford Noble for helpful comments on the manuscript.
Author information
Authors and Affiliations
Contributions
All authors contributed to all aspects of the article.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Reviews Genetics thanks Shaun Mahony and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Glossary
- Features
-
Scalar inputs to a machine learning model.
- Local interpretation
-
The task of understanding a model’s prediction for a single input.
- Global interpretation
-
The task of understanding how a model makes predictions across all inputs.
- Sequence-to-activity models
-
A class of learning tasks that takes a DNA sequence as input and predicts a property of the activity of that sequence, such as transcription factor binding or chromatin accessibility in a cell type of interest.
- Convolutional neural networks
-
(CNNs). A neural network architecture that includes convolutional nodes.
- Recurrent neural networks
-
(RNNs). A type of neural network architecture in which nodes are arranged in a chain along a sequential input such as a DNA sequence.
- Layers
-
Sets of neural network nodes that take input from nodes of the previous layer and output to nodes of the subsequent layer.
- Convolutional nodes
-
(Also known as filters). A type of neural network node that takes input from a short contiguous sequence of nodes, usually 3–20 bp in sequence-to-activity models.
- Nodes
-
(Also known as units and artificial neurons). The basic units of a neural network. They take input from other nodes and output scalar values to other nodes.
- Regulatory element
-
Region in genomic DNA that can contribute to gene regulation.
- Attention mechanism
-
A component of a neural network that can learn to adaptively prioritize (that is, pay attention to) certain parts of an input by weighting.
- Attention weights
-
Weights learned by the attention mechanism.
- Drop-out
-
A form of regularization typically used during training of neural networks in which activations from subsets of hidden units are zeroed out.
- Overfitting
-
The case when a machine learning model is specific to its training set and does not generalize to other inputs.
- Labels
-
The target outputs of a classification model.
- One-hot encoding
-
The process of converting a DNA letter into a length-4 vector such that one position is set to 1 and the others are set to 0, for use as input to a neural network.
- Attribution score
-
An importance score assigned to a given input feature by a post-hoc local interpretation method.
- Attribution map
-
(Also known as saliency map or relevance map). An estimate of how much each input feature contributes to the output, produced by certain local interpretation methods.
- Rectified linear unit
-
(ReLU). A common type of nonlinear activation function applied to the output of hidden units, which zeros-out the negative part of the output.
- Self-attention
-
A type of attention mechanism in which every part of the input is compared with every other part, including itself.
- Activation function
-
A function applied to the output of neurons, typically to model non-linearity.
- Regularization
-
A common machine learning scheme that controls model expressivity by including a term in the objective function that penalizes model complexity.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Novakovsky, G., Dexter, N., Libbrecht, M.W. et al. Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat Rev Genet 24, 125–137 (2023). https://doi.org/10.1038/s41576-022-00532-2
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41576-022-00532-2
This article is cited by
-
Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference
Genome Biology (2024)
-
Harnessing deep learning for population genetic inference
Nature Reviews Genetics (2024)
-
AIRE relies on Z-DNA to flag gene targets for thymic T cell tolerization
Nature (2024)
-
Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023
Applied Intelligence (2024)
-
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
BMC Bioinformatics (2023)