| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_a5b0877a9c9676afa05da7ee97974963 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2333-91051
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2333-471
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2800-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2800-368
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61K9-0034
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61K9-0019
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2030-8831
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2333-976 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61K31-57
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61P15-06
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61K45-06
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N30-7233
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N33-5076
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N33-6848
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N33-689
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H10-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H20-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B5-20 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B5-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B40-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N20-00 |
| filingDate |
2020-07-31-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_551efa5af8526947d0b7835a48162c4d
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_6b8d4eaea23dc731026bca7c5b264c82
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_0dc3d655c05ba898918519795ea41d20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_13179a93945d64730c7b1159e81eeec3 |
| publicationDate |
2021-02-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-2021057039-A1 |
| titleOfInvention |
Systems and methods of using machine learning analysis to stratify risk of spontaneous preterm birth |
| abstract |
The present disclosure relates to systems and methods of using machine learning analysis to stratify the risk of spontaneous preterm birth (SPTB). In some variations, to select informative markers that differentiate SPTB from term deliveries, a processed quantification data of the markers can be subjected to univariate receiver operating characteristic (ROC) curve analysis. A Differential Dependency Network (DDN) can then applied in order to extract co-expression patterns among the markers. In order to assess the complementary values among selected markers and the range of their relevant performance, multivariate linear models can be derived and evaluated using bootstrap resampling. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114047277-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113791224-A
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11692983-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2021156832-A1 |
| priorityDate |
2018-01-31-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |