DANN: a deep learning approach for annotating the pathogenicity of genetic variants
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ID: 298972
2014
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Abstract
Summary: Annotating genetic variants, especially non-coding variants, for the purpose of identifying pathogenic variants remains a challenge. Combined annotation-dependent depletion (CADD) is an algorithm designed to annotate both coding and non-coding variants, and has been shown to outperform other annotation algorithms. CADD trains a linear kernel support vector machine (SVM) to differentiate evolutionarily derived, likely benign, alleles from simulated, likely deleterious, variants. However, SVMs cannot capture non-linear relationships among the features, which can limit performance. To address this issue, we have developed DANN. DANN uses the same feature set and training data as CADD to train a deep neural network (DNN). DNNs can capture non-linear relationships among features and are better suited than SVMs for problems with a large number of samples and features. We exploit Compute Unified Device Architecture-compatible graphics processing units and deep learning techniques such as dropout and momentum training to accelerate the DNN training. DANN achieves about a 19% relative reduction in the error rate and about a 14% relative increase in the area under the curve (AUC) metric over CADD’s SVM methodology. Availability and implementation: All data and source code are available at https://cbcl.ics.uci.edu/public_data/DANN/. Contact: xhx@ics.uci.edu
| Reference Key |
openalex_W2011582941
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|---|---|
| Authors | Daniel Quang, Yifei Chen, Xiaohui Xie |
| Journal | BMC Bioinformatics |
| Year | 2014 |
| DOI |
10.1093/bioinformatics/btu703
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| URL | |
| Keywords | Keywords not found |
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