MACHINE-LEARNING TECHNIQUES FOR PREDICTING SURFACE-PRESENTING PEPTIDES
The disclosure provides methods for predicting surface-presenting peptides using binding and surface-presentation characteristics. The method can include accessing a trained machine-learning model that is configured to generate an output that indicates an extent to which the one or more expression l...
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| creator | HAUDENSCHILD, Christian LEVY, Eric BARTHA, Gabor MILANI, Pamela SALDIVAR, Juan-Sebastian MCNITT, Paul HARRIS, Jason PYKE, Rachel, Marty CLARK, Michael ABBOTT, Charles, Wilbur, III MELLACHERUVU, Dattatreya CHEN, Richard TANDON, Prateek PHILLIPS, Nick ZHANG, Simo, V WEST, John MORRA, Massimo DESAI, Sejal MCCLORY, Rena POWER, Robert BOYLE, Sean, Michael |
| description | The disclosure provides methods for predicting surface-presenting peptides using binding and surface-presentation characteristics. The method can include accessing a trained machine-learning model that is configured to generate an output that indicates an extent to which the one or more expression levels and the one or more peptide-presentation metrics are related in accordance with a population-level relationship between expression and presentation. For each peptide of the set of peptides for a tissue sample, a score can be determined using the machine-learning model and genomic and transcriptomic data corresponding to the peptide. The score is predictive of whether a corresponding peptide is a surface-presenting peptide that binds to an MHC molecule and is presented on a cell surface. |
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The method can include accessing a trained machine-learning model that is configured to generate an output that indicates an extent to which the one or more expression levels and the one or more peptide-presentation metrics are related in accordance with a population-level relationship between expression and presentation. For each peptide of the set of peptides for a tissue sample, a score can be determined using the machine-learning model and genomic and transcriptomic data corresponding to the peptide. The score is predictive of whether a corresponding peptide is a surface-presenting peptide that binds to an MHC molecule and is presented on a cell surface.</description><language>eng ; fre ; ger</language><subject>BEER ; BIOCHEMISTRY ; CHEMISTRY ; COMPOSITIONS OR TEST PAPERS THEREFOR ; COMPOSITIONS THEREOF ; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL ORENZYMOLOGICAL PROCESSES ; CULTURE MEDIA ; ENZYMOLOGY ; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS ; INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES ; MEASURING ; MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEICACIDS OR MICROORGANISMS ; METALLURGY ; MICROBIOLOGY ; MICROORGANISMS OR ENZYMES ; MUTATION OR GENETIC ENGINEERING ; PHYSICS ; PROCESSES OF PREPARING SUCH COMPOSITIONS ; PROPAGATING, PRESERVING OR MAINTAINING MICROORGANISMS ; SPIRITS ; TESTING ; VINEGAR ; WINE</subject><creationdate>2024</creationdate><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240807&DB=EPODOC&CC=EP&NR=4168569A4$$EHTML$$P50$$Gepo$$Hfree_for_read</linktohtml><link.rule.ids>230,308,778,883,25547,76298</link.rule.ids><linktorsrc>$$Uhttps://worldwide.espacenet.com/publicationDetails/biblio?FT=D&date=20240807&DB=EPODOC&CC=EP&NR=4168569A4$$EView_record_in_European_Patent_Office$$FView_record_in_$$GEuropean_Patent_Office$$Hfree_for_read</linktorsrc></links><search><creatorcontrib>HAUDENSCHILD, Christian</creatorcontrib><creatorcontrib>LEVY, Eric</creatorcontrib><creatorcontrib>BARTHA, Gabor</creatorcontrib><creatorcontrib>MILANI, Pamela</creatorcontrib><creatorcontrib>SALDIVAR, Juan-Sebastian</creatorcontrib><creatorcontrib>MCNITT, Paul</creatorcontrib><creatorcontrib>HARRIS, Jason</creatorcontrib><creatorcontrib>PYKE, Rachel, Marty</creatorcontrib><creatorcontrib>CLARK, Michael</creatorcontrib><creatorcontrib>ABBOTT, Charles, Wilbur, III</creatorcontrib><creatorcontrib>MELLACHERUVU, Dattatreya</creatorcontrib><creatorcontrib>CHEN, Richard</creatorcontrib><creatorcontrib>TANDON, Prateek</creatorcontrib><creatorcontrib>PHILLIPS, Nick</creatorcontrib><creatorcontrib>ZHANG, Simo, V</creatorcontrib><creatorcontrib>WEST, John</creatorcontrib><creatorcontrib>MORRA, Massimo</creatorcontrib><creatorcontrib>DESAI, Sejal</creatorcontrib><creatorcontrib>MCCLORY, Rena</creatorcontrib><creatorcontrib>POWER, Robert</creatorcontrib><creatorcontrib>BOYLE, Sean, Michael</creatorcontrib><title>MACHINE-LEARNING TECHNIQUES FOR PREDICTING SURFACE-PRESENTING PEPTIDES</title><description>The disclosure provides methods for predicting surface-presenting peptides using binding and surface-presentation characteristics. The method can include accessing a trained machine-learning model that is configured to generate an output that indicates an extent to which the one or more expression levels and the one or more peptide-presentation metrics are related in accordance with a population-level relationship between expression and presentation. For each peptide of the set of peptides for a tissue sample, a score can be determined using the machine-learning model and genomic and transcriptomic data corresponding to the peptide. The score is predictive of whether a corresponding peptide is a surface-presenting peptide that binds to an MHC molecule and is presented on a cell surface.</description><subject>BEER</subject><subject>BIOCHEMISTRY</subject><subject>CHEMISTRY</subject><subject>COMPOSITIONS OR TEST PAPERS THEREFOR</subject><subject>COMPOSITIONS THEREOF</subject><subject>CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL ORENZYMOLOGICAL PROCESSES</subject><subject>CULTURE MEDIA</subject><subject>ENZYMOLOGY</subject><subject>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</subject><subject>INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES</subject><subject>MEASURING</subject><subject>MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEICACIDS OR MICROORGANISMS</subject><subject>METALLURGY</subject><subject>MICROBIOLOGY</subject><subject>MICROORGANISMS OR ENZYMES</subject><subject>MUTATION OR GENETIC ENGINEERING</subject><subject>PHYSICS</subject><subject>PROCESSES OF PREPARING SUCH COMPOSITIONS</subject><subject>PROPAGATING, PRESERVING OR MAINTAINING MICROORGANISMS</subject><subject>SPIRITS</subject><subject>TESTING</subject><subject>VINEGAR</subject><subject>WINE</subject><fulltext>true</fulltext><rsrctype>patent</rsrctype><creationdate>2024</creationdate><recordtype>patent</recordtype><sourceid>EVB</sourceid><recordid>eNrjZHDzdXT28PRz1fVxdQzy8_RzVwhxdfbw8wwMdQ1WcPMPUggIcnXxdA4ByQSHBrk5OrvqAoWCXf3AQgGuASGeLq7BPAysaYk5xam8UJqbQcHNNcTZQze1ID8-tbggMTk1L7Uk3jXAxNDMwtTM0tHEmAglAH00LFc</recordid><startdate>20240807</startdate><enddate>20240807</enddate><creator>HAUDENSCHILD, Christian</creator><creator>LEVY, Eric</creator><creator>BARTHA, Gabor</creator><creator>MILANI, Pamela</creator><creator>SALDIVAR, Juan-Sebastian</creator><creator>MCNITT, Paul</creator><creator>HARRIS, Jason</creator><creator>PYKE, Rachel, Marty</creator><creator>CLARK, Michael</creator><creator>ABBOTT, Charles, Wilbur, III</creator><creator>MELLACHERUVU, Dattatreya</creator><creator>CHEN, Richard</creator><creator>TANDON, Prateek</creator><creator>PHILLIPS, Nick</creator><creator>ZHANG, Simo, V</creator><creator>WEST, John</creator><creator>MORRA, Massimo</creator><creator>DESAI, Sejal</creator><creator>MCCLORY, Rena</creator><creator>POWER, Robert</creator><creator>BOYLE, Sean, Michael</creator><scope>EVB</scope></search><sort><creationdate>20240807</creationdate><title>MACHINE-LEARNING TECHNIQUES FOR PREDICTING SURFACE-PRESENTING PEPTIDES</title><author>HAUDENSCHILD, Christian ; LEVY, Eric ; BARTHA, Gabor ; MILANI, Pamela ; SALDIVAR, Juan-Sebastian ; MCNITT, Paul ; HARRIS, Jason ; PYKE, Rachel, Marty ; CLARK, Michael ; ABBOTT, Charles, Wilbur, III ; MELLACHERUVU, Dattatreya ; CHEN, Richard ; TANDON, Prateek ; PHILLIPS, Nick ; ZHANG, Simo, V ; WEST, John ; MORRA, Massimo ; DESAI, Sejal ; MCCLORY, Rena ; POWER, Robert ; BOYLE, Sean, Michael</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-epo_espacenet_EP4168569A43</frbrgroupid><rsrctype>patents</rsrctype><prefilter>patents</prefilter><language>eng ; fre ; ger</language><creationdate>2024</creationdate><topic>BEER</topic><topic>BIOCHEMISTRY</topic><topic>CHEMISTRY</topic><topic>COMPOSITIONS OR TEST PAPERS THEREFOR</topic><topic>COMPOSITIONS THEREOF</topic><topic>CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL ORENZYMOLOGICAL PROCESSES</topic><topic>CULTURE MEDIA</topic><topic>ENZYMOLOGY</topic><topic>INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS</topic><topic>INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES</topic><topic>MEASURING</topic><topic>MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEICACIDS OR MICROORGANISMS</topic><topic>METALLURGY</topic><topic>MICROBIOLOGY</topic><topic>MICROORGANISMS OR ENZYMES</topic><topic>MUTATION OR GENETIC ENGINEERING</topic><topic>PHYSICS</topic><topic>PROCESSES OF PREPARING SUCH COMPOSITIONS</topic><topic>PROPAGATING, PRESERVING OR MAINTAINING MICROORGANISMS</topic><topic>SPIRITS</topic><topic>TESTING</topic><topic>VINEGAR</topic><topic>WINE</topic><toplevel>online_resources</toplevel><creatorcontrib>HAUDENSCHILD, Christian</creatorcontrib><creatorcontrib>LEVY, Eric</creatorcontrib><creatorcontrib>BARTHA, Gabor</creatorcontrib><creatorcontrib>MILANI, Pamela</creatorcontrib><creatorcontrib>SALDIVAR, Juan-Sebastian</creatorcontrib><creatorcontrib>MCNITT, Paul</creatorcontrib><creatorcontrib>HARRIS, Jason</creatorcontrib><creatorcontrib>PYKE, Rachel, Marty</creatorcontrib><creatorcontrib>CLARK, Michael</creatorcontrib><creatorcontrib>ABBOTT, Charles, Wilbur, III</creatorcontrib><creatorcontrib>MELLACHERUVU, Dattatreya</creatorcontrib><creatorcontrib>CHEN, Richard</creatorcontrib><creatorcontrib>TANDON, Prateek</creatorcontrib><creatorcontrib>PHILLIPS, Nick</creatorcontrib><creatorcontrib>ZHANG, Simo, V</creatorcontrib><creatorcontrib>WEST, John</creatorcontrib><creatorcontrib>MORRA, Massimo</creatorcontrib><creatorcontrib>DESAI, Sejal</creatorcontrib><creatorcontrib>MCCLORY, Rena</creatorcontrib><creatorcontrib>POWER, Robert</creatorcontrib><creatorcontrib>BOYLE, Sean, Michael</creatorcontrib><collection>esp@cenet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>HAUDENSCHILD, Christian</au><au>LEVY, Eric</au><au>BARTHA, Gabor</au><au>MILANI, Pamela</au><au>SALDIVAR, Juan-Sebastian</au><au>MCNITT, Paul</au><au>HARRIS, Jason</au><au>PYKE, Rachel, Marty</au><au>CLARK, Michael</au><au>ABBOTT, Charles, Wilbur, III</au><au>MELLACHERUVU, Dattatreya</au><au>CHEN, Richard</au><au>TANDON, Prateek</au><au>PHILLIPS, Nick</au><au>ZHANG, Simo, V</au><au>WEST, John</au><au>MORRA, Massimo</au><au>DESAI, Sejal</au><au>MCCLORY, Rena</au><au>POWER, Robert</au><au>BOYLE, Sean, Michael</au><format>patent</format><genre>patent</genre><ristype>GEN</ristype><title>MACHINE-LEARNING TECHNIQUES FOR PREDICTING SURFACE-PRESENTING PEPTIDES</title><date>2024-08-07</date><risdate>2024</risdate><abstract>The disclosure provides methods for predicting surface-presenting peptides using binding and surface-presentation characteristics. The method can include accessing a trained machine-learning model that is configured to generate an output that indicates an extent to which the one or more expression levels and the one or more peptide-presentation metrics are related in accordance with a population-level relationship between expression and presentation. For each peptide of the set of peptides for a tissue sample, a score can be determined using the machine-learning model and genomic and transcriptomic data corresponding to the peptide. The score is predictive of whether a corresponding peptide is a surface-presenting peptide that binds to an MHC molecule and is presented on a cell surface.</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | BEER BIOCHEMISTRY CHEMISTRY COMPOSITIONS OR TEST PAPERS THEREFOR COMPOSITIONS THEREOF CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL ORENZYMOLOGICAL PROCESSES CULTURE MEDIA ENZYMOLOGY INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES MEASURING MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEICACIDS OR MICROORGANISMS METALLURGY MICROBIOLOGY MICROORGANISMS OR ENZYMES MUTATION OR GENETIC ENGINEERING PHYSICS PROCESSES OF PREPARING SUCH COMPOSITIONS PROPAGATING, PRESERVING OR MAINTAINING MICROORGANISMS SPIRITS TESTING VINEGAR WINE |
| title | MACHINE-LEARNING TECHNIQUES FOR PREDICTING SURFACE-PRESENTING PEPTIDES |
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