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Multiple kernel-based fuzzy system for identifying enhancers
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AI
DOI：10.1016/j.eswa.2024.125981.png)
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Abstract
En 中文
As a remote regulatory element of DNA, enhancers play a pivotal role in embryonic development, regulating gene expression, homeostasis and disease occurrence in a variety of biological processes. Identifying enhancers facilitates the exploration of biological processes and mechanisms. However, experimental methods utilized to identify enhancers are high time investment and expensive. Currently, many computational methods have been developed for large-scale enhancer identification. In this research, a new computational method called MCMKHFIS was proposed to identify enhancers. The method is built upon kernelized high-order fuzzy inference system (KHFIS) and incorporates multiple kernel learning (MKL) techniques and mixture correntropy (MC) loss. We use MKL to identify the feature space that is conducive to sample representation, which is then used to construct the fuzzy kernel matrix. To further strengthen the performance and robustness of the method, we employ MC to reconstruct the objective function of the model. From the experimental results reported on two enhancer datasets, our method demonstrates excellent classification performance.
Keywords:
Enhancer
Bioinformatics
Fuzzy system
Multiple kernel learning
Mixture correntropy
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W