Sequential regulatory activity prediction across chromosomes with convolutional neural networks
By David R. Kelley, Yakir A. Reshef, Maxwell L. Bileschi, David Belanger, Cory Y. McLean, Jasper Snoek. bioRxiv, 2017
513 citations recorded by the source corpus.
Models for predicting phenotypic outcomes from genotypes have important applications to understanding genomic function and improving human health. Here, we develop a machine-learning system to predict cell type-specific epigenetic and transcriptional profiles in large mammalian genomes from DNA sequence alone. Using convolutional neural networks, this system identifies promoters and distal regulatory elements and synthesizes their content to make effective gene expression predictions. We show that model predictions for the influence of genomic variants on gene expression align well to causal variants underlying eQTLs in human populations and can be useful for generating mechanistic hypotheses to enable fine mapping of disease loci.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/09f804b59c8fc356273f98b273290ad6fbdb443b
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In Molecular Biology
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Citations in the corpus
- Artificial intelligence in clinical and genomic diagnostics (2019, background): “Deep-learning-based approaches have also substantially improved our ability to detect regulatory elements [65, 66] and to predict the influence of genetic variation on those elements.”
- Toward Interpretable Machine Learning: Transparent Deep Neural Networks and Beyond (2020, methodology): “The work by [69] uses interpretable ML to understand the activity prediction across chromosomes, whereas [27] uses these methods for understanding automated decisions on behavioral biometrics.”
- Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications (2021, methodology): “[87] uses a gradient-based explanation technique to understand the activity prediction across chromosomes, whereas Chong et al.”
- The Nucleotide Transformer: Building and Evaluating Robust Foundation Models for Human Genomics (2024, methodology): “Along this line of work, specialized deep learning (DL) models were trained to identify meaningful patterns of DNA, for example, to predict gene expression from DNA sequences alone [9, 10, 11, 12, 13], with recent work combining convolutional neural ∗Equal Supervision †Corresponding authors: t.”