Combinatorics

Adaptive neuro fuzzy inference system

Inference

Mathematics

Kernel (algebra)

Biology

Gene

Machine learning

Gene expression

Artificial intelligence

Fuzzy control system

DNA methylation

Computer science

Computational biology

Fuzzy inference system

Epigenetics

Genetics

Fuzzy logic

Robustness (evolution)

Chromatin

IEEE Transactions on Fuzzy Systems

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.

C-Loss Based Higher Order Fuzzy Inference Systems for Identifying DNA N4-Methylcytosine Sites