DeepSVM-fold: protein fold recognition by combining support vector machines and pairwise sequence similarity scores generated by deep learning networks. (28th October 2019)

• Record Type: Journal Article
• Authors: Liu, Bin; Li, Chen-Chen; Yan, Ke
• Is Part Of: Briefings in bioinformatics. Volume 21:Number 5(2020)
• Page Start: 1733
• Page End: 1741
• Publication Date: 2019-10-28
• Subjects: protein fold recognition -- convolutional neural network -- long short-term memory -- support vector machine -- pairwise sequence similarity scores; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbz098
• Languages: English
• ISSNs: 1467-5463

Abstract

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.