Name
Abstract | ||
|---|---|---|
In this study, we proposed a new denoising method for sequence-based CNV detection based on a signal processing technique. Existing CNV detection algorithms identify many false CNV segments and fail in detecting short CNV segments due to noise and biases. Employing an effective and efficient denoising method can significantly enhance the detection accuracy of the CNV segmentation algorithms. Advanced denoising methods from the signal processing field can be employed to implement such algorithms. We showed that non-linear denoising methods that consider sparsity and piecewise constant characteristics of CNV data result in better performance in CNV detection. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2332-x | BMC Bioinformatics |
Keywords | Field | DocType |
Copy number variation,Denoising,Next generation sequencing,Signal processing,Taut string,Total variation | Noise reduction,Signal processing,Biology,Pattern recognition,Copy-number variation,DNA sequencing,Artificial intelligence,Active noise control,Genetics | Journal |
Volume | Issue | ISSN |
19-S | 11 | 1471-2105 |
Citations | PageRank | References |
0 | 0.34 | 21 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fatima Zare | 1 | 0 | 2.37 |
| Abdelrahman Hosny | 2 | 4 | 0.81 |
| Sheida Nabavi | 3 | 18 | 8.68 |