Research Area:  Machine Learning

Abstract:

Protein fold recognition is critical for studying the structures and functions of proteins. The existing protein fold recognition approaches failed to efficiently calculate the pairwise sequence similarity scores of the proteins in the same fold sharing low sequence similarities. Furthermore, the existing feature vectorization strategies are not able to measure the global relationships among proteins from different protein folds. In this article, we proposed a new computational predictor called DeepSVM-fold for protein fold recognition by introducing a new feature vector based on the pairwise sequence similarity scores calculated from the fold-specific features extracted by deep learning networks. The feature vectors are then fed into a support vector machine to construct the predictor. Experimental results on the benchmark dataset (LE) show that DeepSVM-fold obviously outperforms all the other competing methods.

Keywords:  
Protein fold recognition
Convolutional neural network
Long short-term memory
Support vector machine
Pairwise sequence Similarity scores

Author(s) Name:  Bin Liu, Chen-Chen Li , Ke Yan

Publisher name:  Oxford Academic

DOI:  10.1093/bib/bbz098

Volume Information:  Volume 21

Paper Link:   https://academic.oup.com/bib/article/21/5/1733/5593805