http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-102478562-B
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_caacf483b6be9ae2d2677103a70559db http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_2832a40f112f92bf8d7655e3809f5815 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N30-88 |
| filingDate | 2010-11-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2014-07-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e2f5fd2908fd1f93f5199a7deb2741fc http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_996930b1f83756a55e90924e62ca9e9c http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_eff9deef4d2d23a1cb39cf59740fd173 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a79dc07cbc5bc8f26379f8a79de8ed79 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d6d7c3b37012085e28e32b5595f9a4e1 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_aac99a924cc092f7fcffc965c76b42c3 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_09dcdc86690db1ded502e2d07d16910b |
| publicationDate | 2014-07-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-102478562-B |
| titleOfInvention | Method for screening ovarian cancer body fluid prognostic marker by L-EDA |
| abstract | The invention discloses a method for screening an ovarian cancer prognostic marker from a body fluid metabolome profile by modified estimation of distribution algorithms (L-EDA). A metabolome profile is obtained by analyzing a body fluid metabolite by using a liquid chromatograph-mass spectrometer; a probability distribution model is established to analyze the metabolome profile; and a potential ovarian cancer prognostic marker is screened. Being different from traditional estimation of distribution algorithms, L-EDA limits the size of a candidate attribute subset generated during iterative search, provides a new probability distribution model update strategy, allows the evaluation of the attributes to be more accurate and reasonable, and improves the execution efficiency of the algorithms. The attribute subset screened by L-EDA can reflect the characteristics among groups of the metabolome profile data; a support vector machine (SVM) classification model is established for cross validation analysis, and the correct rate reaches 99.06%. |
| priorityDate | 2010-11-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 30.