Name
Abstract | ||
|---|---|---|
The 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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1093/bioinformatics/btu703 | BIOINFORMATICS |
Field | DocType | Volume |
Source code,Computer science,Coding (social sciences),Artificial intelligence,Deep learning,Artificial neural network,Kernel (linear algebra),Graphics,Pattern recognition,Word error rate,Support vector machine,Bioinformatics,Machine learning | Journal | 31 |
Issue | ISSN | Citations |
5 | 1367-4803 | 43 |
PageRank | References | Authors |
2.11 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Quang | 1 | 47 | 3.23 |
| Yifei Chen | 2 | 48 | 2.55 |
| Xiaohui Xie | 3 | 61 | 5.50 |