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Prediction of Affinity Between MHC Molecules and Peptide-ligands Using Convolutional Neural Networks

Student: Layko Rudolph

Supervisor: Olga V. Valba

Faculty: HSE Tikhonov Moscow Institute of Electronics and Mathematics (MIEM HSE)

Educational Programme: Applied Mathematics (Bachelor)

Year of Graduation: 2018

In this work we propose novel neural network architecture and effective amino-acid representation approach for tackling problem of predicting binding affinity prediction of peptide-ligands and MHC molecules. Interaction between peptide and MHC is significant problem in modern immunoinformatics. Current state-of-the-art results are demonstrated by NetMHCpan, NetMHC\cite{NetMHC4}\cite{netmhcpan4} and MHCflurry\cite {mhcflurry} approaches, which are broadly using shallow neural networks. Series of conducted experiments following testing procedure described in \cite{mhcflurry} with proposed neural network configuration showed that we achieved best performance comparing to current standart tools for prediction.

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