| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_3c35a206188eedb4e5e4f7e22fa5b067 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-047
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-2255
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-0445
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B30-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-044
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F16-24534
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16B40-20 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B30-10
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-22
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16B40-20
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N33-50
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F16-2453
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N33-48 |
| filingDate |
2021-11-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2022-12-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c6e0018ff0c74489db2b53b40f749bd6
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c57e9db2714fe431c776ac73fa6a1c89 |
| publicationDate |
2022-12-20-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-11532378-B2 |
| titleOfInvention |
Protein database search using learned representations |
| abstract |
A method for efficient search of protein sequence databases for proteins that have sequence, structural, and/or functional homology with respect to information derived from a search query. The method involves transforming the protein sequences into vector representations and searching in a vector space. Given a database of protein sequences and a learned embedding model, the embedding model is applied to each amino acid sequence to transform it into a sequence of vector representations. A query sequence is also transformed into a sequence of vector representations, preferably using the same learned embedding model. Once the query has been embedded in this manner, proteins are retrieved from the database based on distance between the query embedding and the protein embeddings contained within the database. Rapid and accurate search of the vector space is carried out using exact search using metric data structures, or approximate search using locality sensitive hashing. |
| priorityDate |
2020-11-23-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |