http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2020243526-A1
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_126b1f3edc33b69783984fe0a89f1dbf |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C12Q1-6883 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C12Q1-6886 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B30-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-30 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B30-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B20-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C12Q1-6827 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-36 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B40-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B45-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/C12Q1-6809 |
| filingDate | 2020-05-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f7b5a2ddad050d2dab62090f649944e7 |
| publicationDate | 2020-12-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | WO-2020243526-A1 |
| titleOfInvention | Estimating predisposition for disease based on classification of artificial image objects created from omics data |
| abstract | Methods and systems are provided for classifying genetic variant and gene function and/or expression data, as well as DNA methylation, epigenomics, proteomics, metabolomics, microbiomics, and other biological/omics data into one or more uni- or multi-dimensional artificial image objects (AIOs) for image analyses. AIOs are composed of a plurality of cells, each being assigned a specific variant. Each variant is assigned a specific value. The graphic pixel signals from AIOs generated from a population of subjects each possessing a particular trait (or not) are analyzed and/or trained collectively with Machine Learning (ML) or other Artificial Intelligence (AI) algorithms. The trained algorithm then detects characteristic signatures of the trait from the AIO to determine whether a subject possesses the trait or not, thereby affording rapid and accurate detection and better treatment. Traits include, but are not limited to, diseases such as mental illness, cancer, heart disease, and other biological conditions. |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113159238-A |
| priorityDate | 2019-05-31-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 111.