Methods and systems for improved major histocompatibility complex (MHC)-peptide binding prediction of neoepitopes using a recurrent neural network encoder and attention weighting

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United States of America Patent

PATENT NO 11557375
APP PUB NO 20210202043A1
SERIAL NO

17059157

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Abstract

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Techniques are provided for predicting MHC-peptide binding affinity. A plurality of training peptide sequences is obtained, and a neural network model is trained to predict MHC-peptide binding affinity using the training peptide sequences. An encoder of the neural network model comprising an RNN is configured to process an input training peptide sequence to generate a fixed-dimension encoding output by applying a final hidden state of the RNN at intermediate state outputs of the RNN to generate attention weighted outputs, and linearly combining the attention weighted outputs. A fully connected layer following the encoder is configured to process the fixed-dimension encoding output to generate an MHC-peptide binding affinity prediction output. A computing device is configured to use the trained neural network to predict MHC-peptide binding affinity for a test peptide sequence.

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Patent Owner(s)

  • NANTOMICS, LLC

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Inventor(s)

Inventor Name Address # of filed Patents Total Citations
Sudol, Jeremi New York, US 34 737
Wnuk, Kamil Playa del Rey, US 46 845

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