Identification of DNA N4-methylcytosine sites via multi-view kernel sparse representation model

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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IEEE Transactions on Artificial Intelligence, articlenumber 9809784
Identifying DNA N4-methylcytosine (4mC) sites is of great significance in biological research, such as chromatin structure, DNA stability, DNA-protein interaction and controlling gene expression. However, the traditional sequencing technology to identify 4mC sites is very time-consuming. In order to detect 4mC sites, we develop a multi-view learning method for achieving more effectively via merging multiple feature spaces. Furthermore, we think about whether the multi-view learning method can improve the across species classification ability by fusing data of multiple species. In our study, we propose a multi-view Laplacian kernel sparse representation-based classifier, called MvLapKSRC-HSIC. First, we make use of three feature extraction methods (PSTNP, NCP, DPP) to extract the DNA sequence features. MvLapKSRC-HSIC uses a kernel sparse representation-based classifier with graph regularization. In order to maintain the independence between various views, we add a multi-view regularization term constructed by Hilbert-Schmidt independence criterion (HSIC). In the experiments, MvLapKSRC-HSIC is applied on six datasets, so as to compare with other popular methods in single species and cross-species experiments. All experimental results show that MvLapKSRC-HSIC is superior to other outstanding methods on both single species and cross-species. Importantly, MvLapKSRC-HSIC can identify a series of potential DNA 4mC sites, which have not yet been experimentally evaluate on multiple species and merit further research.
| openaire: EC/H2020/101016775/EU//INTERVENE
DNA, Kernel, Feature extraction, Training, Laplace equations, Learning systems, Support vector machines
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Ai, C, Tiwari, P, Yang, H, Ding, Y, Tang, J & Guo, F 2023, ' Identification of DNA N4-methylcytosine sites via multi-view kernel sparse representation model ', IEEE Transactions on Artificial Intelligence, vol. 4, no. 5, 9809784, pp. 1236-1245 .