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DeepSVM-fold: protein fold recognition by combining support vector machines and pairwise sequence similarity scores generated by deep learning networks
- Source :
- Briefings in Bioinformatics. 21:1733-1741
- Publication Year :
- 2019
- Publisher :
- Oxford University Press (OUP), 2019.
-
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.
- Subjects :
- Protein Folding
Support Vector Machine
Computer science
Feature vector
0206 medical engineering
02 engineering and technology
Convolutional neural network
03 medical and health sciences
Deep Learning
Similarity (network science)
Feature (machine learning)
Molecular Biology
030304 developmental biology
0303 health sciences
Sequence
business.industry
Deep learning
Proteins
Pattern recognition
Support vector machine
Pairwise comparison
Artificial intelligence
business
Algorithms
020602 bioinformatics
Information Systems
Subjects
Details
- ISSN :
- 14774054 and 14675463
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- Briefings in Bioinformatics
- Accession number :
- edsair.doi.dedup.....7bad8dcd6a6433b6cfb33278619d2095