Comments (1)
Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory โgrammarโ to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ.
from deep-review.
Related Issues (20)
- Potential Reorganization HOT 7
- Deep Learning for the Life Sciences
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- Hidden bias in the DUD-E dataset leads to misleading performance of deep learning in structure-based virtual screening
- Update subsection: Structure-based prediction of bioactivity HOT 2
- Update 'Imaging applications in healthcare'
- Applications in sleep research HOT 2
- LISA: Towards Learned DNA Sequence Search HOT 1
- DeepArk: modeling cis-regulatory codes of model species with deep learning
- Human Protein Atlas (HPA) Cell Type Prediction using Deep Learning and estimating uncertainty HOT 2
- How can one cite this dynamic review? HOT 3
- Update for 2020? HOT 3
- Add a Systematic Text-Mining Component to the Review? HOT 3
- Titrating gene expression using libraries of systematically attenuated CRISPR guide RNAs HOT 3
- Alpha Fold 2 HOT 1
- feature and labels HOT 1
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from deep-review.