C-Loss Based Higher Order Fuzzy Inference Systems for Identifying DNA N4-Methylcytosine Sites
Authors: Ding, Y., Tiwari, P., Zou, Q., Guo, F. and Pandey, H.M.
Journal: IEEE Transactions on Fuzzy Systems
Volume: 30
Issue: 11
Pages: 4754-4765
eISSN: 1941-0034
ISSN: 1063-6706
DOI: 10.1109/TFUZZ.2022.3159103
Abstract:DNA methylation is an epigenetic marker that plays an important role in the biological processes of regulating gene expression, maintaining chromatin structure, imprinting genes, inactivating X chromosomes, and developing embryos. The traditional detection method is time-consuming. Currently, researchers have used effective computational methods to improve the efficiency of methylation detection. This study proposes a fuzzy model with correntropy induced loss (C-loss) function to identify DNA N4-methylcytosine (4 mC) sites. To improve the robustness and performance of the model, we use kernel method and the C-loss function to build a higher order fuzzy inference systems. To test performance, our model is implemented on six 4 mC and eight University of California Irvine (UCI) datasets. The experimental results show that our model achieves better prediction performance.
https://eprints.bournemouth.ac.uk/36992/
Source: Scopus
C-Loss Based Higher Order Fuzzy Inference Systems for Identifying DNA N4-Methylcytosine Sites
Authors: Ding, Y., Tiwari, P., Zou, Q., Guo, F. and Pandey, H.M.
Journal: IEEE TRANSACTIONS ON FUZZY SYSTEMS
Volume: 30
Issue: 11
Pages: 4754-4765
eISSN: 1941-0034
ISSN: 1063-6706
DOI: 10.1109/TFUZZ.2022.3159103
https://eprints.bournemouth.ac.uk/36992/
Source: Web of Science (Lite)
C-loss based Higher-order Fuzzy Inference Systems for Identifying DNA N4-methylcytosine Sites
Authors: Ding, Y., Tiwari, P., Zou, Q., Guo, F. and Pandey, H.M.
Journal: IEEE Transactions on Fuzzy Systems
Volume: 30
Issue: 11
Pages: 4754-4765
ISSN: 1063-6706
Abstract:DNA methylation is an epigenetic marker, that plays an important role in the biological processes of regulating gene expression, maintaining chromatin structure, imprinting genes, inactivating X chromosomes, and developing embryos. The traditional detection method is time-consuming. Currently, researchers have used effective computational methods to improve the efficiency of methylation detection. This study proposes a fuzzy model with correntropy induced loss (C-loss) function to identify DNA N4-methylcytosine (4mC) sites. To improve the robustness and performance of the model, we use kernel method and the C-loss function to build a higher-order fuzzy inference system (HFIS). To test performance, our model is implemented on six 4mC and eight UCI data sets. The experimental results show that our model achieves better prediction performance.
https://eprints.bournemouth.ac.uk/36992/
Source: BURO EPrints