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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Hauptverfasser: 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
Format: Patent
Sprache:eng ; fre ; ger
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Zusammenfassung: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.