http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113178257-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_2310d6ac8a51b1d782763a9bbcc6deb3 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B20-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-241 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16H50-20 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16H50-20 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B20-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate | 2021-05-31-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c4cacbcbaf0949af9f6f4eb36d4fee0c |
| publicationDate | 2021-07-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-113178257-A |
| titleOfInvention | Training methods for classification models of pulmonary nodules |
| abstract | The present disclosure describes a training method for a classification model of pulmonary nodules, including: obtaining a TCR immune repertoire related to pulmonary nodules from blood samples of a plurality of pulmonary nodules patients with pulmonary nodules, and obtaining a TCR immune repertoire related to the pulmonary nodules Pulmonary nodule patients match personal information and pathological information, amplify the TCR immune repertoire and obtain second-generation high-throughput sequencing sequences, and obtain the DNA sequence of the target gene and its relationship with the target gene based on the second-generation high-throughput sequencing sequence. The sequence type corresponding to the DNA sequence, and the diversity index information is obtained according to the DNA sequence and sequence type of the target gene; the training set and the test set are formed based on the data sample information, and the data sample information includes personal information, pathological information, and diversity index information. In the training process of the classification model, the training set is used to obtain the loss function, the loss function is used to train the classification model, and the test set is used to test the classification model. |
| priorityDate | 2021-05-31-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 22.