| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_ccc9d7a619069c42dde9efbf61558a28 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-088
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N7-01 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N5-01
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-088
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N7-023
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H15-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H70-60
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/A61K39-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N33-574
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/C12Q1-68 |
| filingDate |
2020-01-10-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_862bae67d2bad3c86c94f1eec56958cf
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_49921d71f066a875b206cf69cf4614dc
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_01a5995a97307f432b281e6f7989e8f0
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9d6349c10566ac8a498ab988daf6948f |
| publicationDate |
2022-01-12-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
EP-3934684-A1 |
| titleOfInvention |
Machine learning in functional cancer assays |
| abstract |
The invention provides methods that use machine learning to discover clinical data patterns that are predictive of disease, such as cancer. Clinical data from across a population is provided as input to a machine learning system. The machine learning system discovers associations in data from a plurality of data sources obtained from a population and correlates the associations to cancer status of patients in the population. The methods may further include providing patient data from an individual and predicting, by the machine learning system, a cancer state (e.g., the presence of cancer and a determination of a stage or progression of the cancer, if present) for the individual when the patient data presents one or more of the discovered associations. |
| priorityDate |
2019-01-10-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |