http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111161793-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B30-10 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B20-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B15-30 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B15-30 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B40-00 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B30-10 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B20-30 |
| filingDate | 2020-01-09-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2023-02-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2023-02-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-111161793-B |
| titleOfInvention | Prediction method of N6-methyladenosine modification site in RNA based on stacking integration |
| abstract | The invention discloses a method for predicting N 6 ‑methyladenosine modification sites in RNA based on stacking integration, and relates to the field of systems biology. The RNA sequence features of 3 species of Saccharomyces cerevisiae, Homo sapiens and Arabidopsis were extracted through 6 feature extraction methods, and the initial feature space of the original data set was obtained through feature fusion; dimensionality reduction was performed using elastic networks to remove redundancy and noise Features, keep the important features related to model classification, and get the best feature set; input the optimal feature subset and the corresponding category labels into the stacking integration for model training, and combine the evaluation indicators to evaluate the prediction performance of the model to get the prediction Model; the RNA sequence to be predicted in the test set is input into the prediction model, and the m 6 A site is predicted and output. The prediction accuracy of this model on the test set reached 92.30% and 87.06%, respectively, and it has good development potential in cross-species prediction, and can be a useful tool for identifying m 6 A sites. |
| priorityDate | 2020-01-09-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 34.