| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_d9e3b6014b56c428f90df9198c169cb7 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-0464
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B20-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B10-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B20-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-09
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B30-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B30-10 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B10-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B40-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B30-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B20-30
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B20-20 |
| filingDate |
2020-11-27-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5d26ddeeb0f544daa346bf59e0e44bca
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fc1df10bb0f24b6964b57a9d83fa8a53
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_ca08c2cce54f4f97221b9e01e6705110
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_d04d2f85ae0a7b1d26893945e0a6bea3
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_609c35d895ac478eef8f28fabb537adb |
| publicationDate |
2021-06-03-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
WO-2021107676-A1 |
| titleOfInvention |
Artificial intelligence-based chromosomal abnormality detection method |
| abstract |
The present invention relates to an artificial intelligence-based chromosomal abnormality detection method, and more specifically, to an artificial intelligence-based chromosomal abnormality detection method using a method that involves: extracting nucleic acids from a biological sample to generate vectorized data on the basis of DNA fragments arranged by acquiring sequence information; and then comparing a reference value and a value calculated by inputting the vectorized data to a trained artificial intelligence model. Rather than using each of values related to reads as an individual normalized value as in existing schemes, which use a step for determining the amount of a chromosome on the basis of a read count, or existing detection methods using the distance concept between arranged reads, the artificial intelligence-based chromosomal abnormality detection method according to the present invention generates vectorized data and analyzes the data using an AI algorithm, and thus is useful in that a similar effect can be exhibited even when read coverage is low. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2023024524-A1 |
| priorityDate |
2019-11-29-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |