MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-Based Protein Structure Prediction
Abstract
Predicting protein properties such as solvent accessibility and secondary structure from its primary amino acid sequence is an important task in bioinformatics. Recently, a few deep learning models have surpassed the traditional window based multilayer perceptron. Taking inspiration from the image classification domain we propose a deep convolutional neural network architecture, MUST-CNN, to predict protein properties. This architecture uses a novel multilayer shift-and-stitch (MUST) technique to generate fully dense per-position predictions on protein sequences. Our model is significantly simpler than the state-of-the-art, yet achieves better results. By combining MUST and the efficient convolution operation, we can consider far more parameters while retaining very fast prediction speeds. We beat the state-of-the-art performance on two large protein property prediction datasets.
Cite
Text
Lin et al. "MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-Based Protein Structure Prediction." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.10007Markdown
[Lin et al. "MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-Based Protein Structure Prediction." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/lin2016aaai-cnn/) doi:10.1609/AAAI.V30I1.10007BibTeX
@inproceedings{lin2016aaai-cnn,
title = {{MUST-CNN: A Multilayer Shift-and-Stitch Deep Convolutional Architecture for Sequence-Based Protein Structure Prediction}},
author = {Lin, Zeming and Lanchantin, Jack and Qi, Yanjun},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {27-34},
doi = {10.1609/AAAI.V30I1.10007},
url = {https://mlanthology.org/aaai/2016/lin2016aaai-cnn/}
}