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Multi-View Kernel Sparse Representation for Identification of Membrane Protein Types
- Source :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics. 20:1234-1245
- Publication Year :
- 2023
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2023.
-
Abstract
- Membrane proteins are the main undertaker of biomembrane functions and play a vital role in many biological activities of organisms. Prediction of membrane protein types has a great help in determining the function of proteins and understanding the interactions of membrane proteins. However, the biochemical experiment is expensive and not suitable for the large-scale identification of membrane protein types. Therefore, computational methods were used to improve the efficiency of biological experiments. Most existing computational methods only use a single feature of protein, or use multiple features but do not integrate these well. In our study, the protein sequence is described via three different views (features), including amino acid composition, evolutionary information and physicochemical properties of amino acids. To exploit information among all views (features), we introduce a coupling strategy for Kernel Sparse Representation based Classification (KSRC) and construct a new model called Multi-view KSRC (MvKSRC). We implement our method on 4 benchmark data sets of membrane proteins. The comparison results indicate that our method is much superior to all existing methods.
- Subjects :
- Applied Mathematics
Genetics
Biotechnology
Subjects
Details
- ISSN :
- 23740043 and 15455963
- Volume :
- 20
- Database :
- OpenAIRE
- Journal :
- IEEE/ACM Transactions on Computational Biology and Bioinformatics
- Accession number :
- edsair.doi.dedup.....38be10a7cfa8f3151c1f880ce86f73c8
- Full Text :
- https://doi.org/10.1109/tcbb.2022.3191325