| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_efa337b90ec21c5ced61bc1e688c820e |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C12Q2600-158
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C12Q1-6886 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H70-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C12Q1-6886
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H20-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B25-10 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H70-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H20-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B40-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/C12Q1-6886 |
| filingDate |
2022-04-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_02cde1e0a95b48e0d45e812c52c4a155
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_aaba619d5931946d1cf71d590929e50d
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_debfed1fd481e17fb0d62e113d66fa02
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c39cfb629b8efb3edc9b39bb679615a7
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_62b61909d87e8c6ebb100da97b15249c
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_bb944bfda12b2b29012ac56cee60806b
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_cef67292a92b4f846b2bc34ebcf1df27
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9616713048d6faaaedcf85b1568990c4
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_784a6aa66a6e9f14494955c759011ceb |
| publicationDate |
2022-11-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2022372580-A1 |
| titleOfInvention |
Machine learning techniques for estimating tumor cell expression in complex tumor tissue |
| abstract |
Techniques for using machine learning to estimate tumor expression levels of genes in tumor cells. The techniques include obtaining expression data for a set of genes comprising a first plurality of genes associated with the tumor cells and a second plurality of genes associated with tumor microenvironment cells; determining the tumor expression levels of the first plurality of genes in the tumor cells using a plurality of machine learning models, the determining comprising: generating a first set of features for the first gene; providing the first set of features as input to the first machine learning model to obtain an output comprising a tumor microenvironment expression level estimate of the first gene in the tumor microenvironment cells; and determining a first tumor expression level for the first gene in the tumor cells using the output of the first machine learning model and a total expression level for the first gene. |
| priorityDate |
2021-04-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |