| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_b0e5f2d2b7b20ecee430e8ac22a95c4f |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2015-1488
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10056
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30024
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2015-1006 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2148
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-698
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-695
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-69
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N15-1429
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N15-1475
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0012 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-00 |
| filingDate |
2019-03-15-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4128a3397e00a10dfd26919191744bdb
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_acba47155e37d5b6e87f86e05fa0e948
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_95a61fb84251abe8f0dc6e54fb804fcf
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a3b07214fd6fe718d7a96567f6bc6a3e |
| publicationDate |
2019-09-19-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
WO-2019178561-A2 |
| titleOfInvention |
Using machine learning and/or neural networks to validate stem cells and their derivatives for use in cell therapy, drug discovery, and diagnostics |
| abstract |
A method is provided for non-invasively predicting characteristics of one or more cells and cell derivatives. The method includes training a machine learning model using at least one of a plurality of training cell images representing a plurality of cells and data identifying characteristics for the plurality of cells. The method further includes receiving at least one test cell image representing at least one test cell being evaluated, the at least one test cell image being acquired non-invasively and based on absorbance as an absolute measure of light, and providing the at least one test cell image to the trained machine learning model. Using machine learning based on the trained machine learning model, characteristics of the at least one test cell are predicted. The method further includes generating, by the trained machine learning model, release criteria for clinical preparations of cells based on the predicted characteristics of the at least one test cell. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111443165-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11680247-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2022235671-A3
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022282201-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2021127345-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11708563-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110838116-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2021256514-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2021247868-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113505849-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113505849-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11643667-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113604544-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111443165-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-110838116-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111709908-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113604544-A |
| priorityDate |
2018-03-16-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |