Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.

ABSTRACT:

Background

Computational scanning of peptide candidates that bind to a specific major histocompatibility complex (MHC) can speed up the peptide-based vaccine development process and therefore various methods are being actively developed. Recently, machine-learning-based methods have generated successful results by training large amounts of experimental data. However, many machine learning-based methods are generally less sensitive in recognizing locally-clustered interactions, which can synergistically stabilize peptide binding. Deep convolutional neural network (DCNN) is a deep learning method inspired by visual recognition process of animal brain and it is known to be able to capture meaningful local patterns from 2D images. Once the peptide-MHC interactions can be encoded into i

SUBMITTER: Han Y 

PROVIDER: S-EPMC5745637 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature