Leveraging Sequence Embedding and Convolutional Neural Network for Protein Function Prediction
Abstract
The capability of accurate prediction of protein functions and properties is essential in the biotechnology industry, e.g. drug development and artificial protein synthesis, etc. The main challenges of protein function prediction are the large label space and the lack of labeled training data. Our method leverages unsupervised sequence embedding and the success of deep convolutional neural network to overcome these challenges. In contrast, most of the existing methods delete the rare protein functions to reduce the label space. Furthermore, some existing methods require additional bio-information (e.g., the 3-dimensional structure of the proteins) which is difficult to be determined in biochemical experiments. Our proposed method significantly outperforms the other methods on the publicly available benchmark using only protein sequences as input. This allows the process of identifying protein functions to be sped up.
Cite
@article{arxiv.2112.00344,
title = {Leveraging Sequence Embedding and Convolutional Neural Network for Protein Function Prediction},
author = {Wei-Cheng Tseng and Po-Han Chi and Jia-Hua Wu and Min Sun},
journal= {arXiv preprint arXiv:2112.00344},
year = {2021}
}
Comments
Published in NeurIPS 2018 Machine Learning for Molecules and Materials Workshop