mldlproject / 2023-i6mA-CNN

Source code and data of the paper entitled "i6mA-CNN: Identifying DNA N6-Methyladenine Sites in Mice using Convolutional Neural Networks with Multiple Kernel Sizes"

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i6mA-CNN: Identifying DNA N6-Methyladenine Sites in Mouse Genomes using Fusion of Multiple Receptive Fields in CNN

T-H Nguyen-Vo, Q. H. Trinh, L. Nguyen, P-U. Nguyen-Hoang, S. Rahardja*, B. P. Nguyen

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Motivation

N6-methyladenine (6mA) is one of the most occurring epigenetic modifications of DNA sequences found in both eukaryotes and prokaryotes. In prokaryotes, 6mA is closely associated with various biochemical processes such as DNA replication, repair, transcription, and cellular defense. While in eukaryotes, biological roles and behaviors of this methylation type have not been fully understood. Therefore, gaining more knowledge about 6mA sites is important and contributes to uncovering the characteristics and unexplored biological functions of 6mA. In our study, we propose an effective computational method called i6mA-CNN using convolutional neural networks featured by multiple receptive fields. The 6mA-determined sequences of Mus musculus (mice) were retrieved from the MethSMRT database and then refined to create a benchmark dataset.

Results

To fairly evaluate the model performance, we performed multiple experiments and compared i6mA-CNN with other methods on the same independent test set. The results indicated that i6mA-CNN had achieved better performance with a value of 0.98 for both the area under the receiver operating characteristic curve and the area under the precision-recall curve.

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Source code and data are available upon request.

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Source code and data of the paper entitled "i6mA-CNN: Identifying DNA N6-Methyladenine Sites in Mice using Convolutional Neural Networks with Multiple Kernel Sizes"