Deep learning-based aberrant splicing detection
US11837324B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Oct 15, 2018 |
| Grant date | Dec 5, 2023 |
| Priority date | — |
| Expiry date | May 23, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F18/24
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The technology disclosed relates to constructing a convolutional neural network-based classifier for variant classification. In particular, it relates to training a convolutional neural network-based classifier on training data using a backpropagation-based gradient update technique that progressively match outputs of the convolutional neural network-based classifier with corresponding ground truth labels. The convolutional neural network-based classifier comprises groups of residual blocks, each group of residual blocks is parameterized by a number of convolution filters in the residual blocks, a convolution window size of the residual blocks, and an atrous convolution rate of the residual blocks, the size of convolution window varies between groups of residual blocks, the atrous convolution rate varies between groups of residual blocks. The training data includes benign training examples and pathogenic training examples of translated sequence pairs generated from benign variants and pathogenic variants.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.