Functional deep neural network for high-dimensional data analysis
US12080426B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 30, 2021 |
| Grant date | Sep 3, 2024 |
| Priority date | — |
| Expiry date | Jun 13, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06T2207/30016
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Various examples of methods and systems are provided related to functional deep neural networks (FDNNs), which can be used for high dimensional data analysis. In one example, a FDNN can be trained with a training set of omic data to produce a trained FDNN model. The likelihood of a condition can be determined based upon output indications of the FDNN corresponding to the one or more phenotypes, with the output indications based upon analysis of omic data including a multi-level omic profile from an individual by the trained FDNN. The FDNN model can include a series of basis functions as layers to capture complexity between the omic data with disease phenotypes. A treatment or prevention strategy for the individual can be identified based at least in part upon the likelihood of the condition.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.