Deep learning-based framework for identifying sequence patterns that cause sequence-specific errors (SSEs)
US12073922B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 8, 2019 |
| Grant date | Aug 27, 2024 |
| Priority date | — |
| Expiry date | Jan 25, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/082
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The technology disclosed presents a deep learning-based framework, which identifies sequence patterns that cause sequence-specific errors (SSEs). Systems and methods train a variant filter on large-scale variant data to learn causal dependencies between sequence patterns and false variant calls. The variant filter has a hierarchical structure built on deep neural networks such as convolutional neural networks and fully-connected neural networks. Systems and methods implement a simulation that uses the variant filter to test known sequence patterns for their effect on variant filtering. The premise of the simulation is as follows: when a pair of a repeat pattern under test and a called variant is fed to the variant filter as part of a simulated input sequence and the variant filter classifies the called variant as a false variant call, then the repeat pattern is considered to have caused the false variant call and identified as SSE-causing.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.