| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_54e331300175b978170471cb57a9f7cc |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C12Q2600-158
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C12Q2600-156
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C12Q1-6869
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C12Q2600-106
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B25-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B20-20 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B30-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/C12Q1-6886
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-20 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/C12Q1-6886
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B40-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B30-10 |
| filingDate |
2022-06-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e2fcc0ed5bc5963e6c12ca04fec8f806
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e080341717a0b7763033aa9a464b83d8 |
| publicationDate |
2022-12-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2022415448-A1 |
| titleOfInvention |
Methods for forecasting clinical course of diffuse large b-cell lymphoma using rna-based biomarkers and machine learning algorithms |
| abstract |
A novel classification strategy is described for forecasting clinical outcomes of Diffuse Large B-cell Lymphoma using targeted RNA sequencing combined with machine learning algorithms. The novel method classifies subjects with DLBCL into subgroups based on the clinical course of their disease and expected survival, rather than on Cell of Origin. To focus on survival, the methods first deploy machine learning and divide the subjects into subgroups based on their overall survival. A modified Bayesian classifier is then used to select genes that can forecast various survival groups, followed by validation of these biomarkers using an independent set of clinical cases. This novel approach for stratifying subjects with DLBCL based on the clinical outcome of rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) chemotherapy can be used to select high responders and low responders to R-CHOP. Low responders may be offered additional or alternative therapies to improve their survival. |
| priorityDate |
2021-06-28-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |