| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_69ece09bafee7e0c49d1f7951a3faa2c |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B25-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61K2039-507
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61K2039-505
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C07K2317-73
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C07K2317-76 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C07K16-22
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61P35-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61K38-217
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C07K16-2827
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B20-00 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61P35-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/C07K16-28
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B40-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/C07K16-22
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61K38-21 |
| filingDate |
2020-09-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4ce8d7f50c60775589fc192d5e16a7f5
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_85efe13d19037f411dd291997cf696d3
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_260bdfdb95f02bd49ea3a672517d03c0 |
| publicationDate |
2021-04-01-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2021098082-A1 |
| titleOfInvention |
Tumor phenotype prediction using genomic analyses indicative of digital-pathology metrics |
| abstract |
A machine-learning model (e.g., a clustering model) may be used to predict a phenotype of a tumor based on expression levels of a set of genes. The set of genes may have been identified using a same or different machine-learning model. The phenotype may include an immune-excluded, immune-desert or an inflamed/infiltrated phenotype. A treatment strategy and/or treatment recommendation may be identified based on the predicted phenotype. |
| priorityDate |
2019-09-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |