| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_c8ed020d9d1eed3acfa2285b2f55de30 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N33-4925
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B2503-40 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-02055
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-0816
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-021
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N7-01
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-14532
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-14542
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-024
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-70
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-163
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/A61B5-4824
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H10-60
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N20-00 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-70
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20 |
| filingDate |
2019-03-12-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e34a7dc7b812eb6bf253de84d6444fef
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_638fdfe7863783ae713fe3a4fd999acc
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d6f930dfda252272bf329c936983827b
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_acef232bad599d0bc8e46f691a7a1b9b
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_26ce593276c5cee5c04423f1310861d6 |
| publicationDate |
2019-11-07-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
WO-2019211575-A1 |
| titleOfInvention |
Method and apparatus for classifying subjects based on time series phenotypic data |
| abstract |
Methods and apparatus for classifying subjects based on time series phenotypic data are disclosed. In one arrangement, a data receiving unit receives a set of first subject-data-units, each first subject-data-unit in the set comprising time series data representing phenotypic information about a different respective one of a plurality of subjects to be classified. A data processing unit processes the set of first subject-data- units to reduce a dimensionality of each first subject-data-unit, thereby obtaining a corresponding set of second subject-data-units having lower dimensionality than the first subject-data-units. The set of second subject-data-units is processed to cluster the second subject-data-units into a plurality of clusters. Each of one or more of the subjects is classified by determining to which cluster a second subject-data-unit corresponding to the subject belongs. The clustering comprises fitting a mean trajectory with error bounds to the time series data of each second subject-data-unit and clustering the resulting fitted mean trajectories with error bounds. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111714094-A |
| priorityDate |
2018-05-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |