标识符
计算机科学
鉴定(生物学)
胶囊
表观遗传学
计算机网络
计算生物学
人工智能
基因
生物
植物
遗传学
作者
Zheyu Zhou,Cuilin Xiao,Jinfen Yin,Jiayi She,Hao Duan,Chunling Liu,Xiuhao Fu,Feifei Cui,Qi Qi,Zilong Zhang
标识
DOI:10.1016/j.compbiomed.2024.108129
摘要
DNA N6-methyladenine (6mA) modifications play a pivotal role in the regulation of growth, development, and diseases in organisms. As a significant epigenetic marker, 6mA modifications extensively participate in the intricate regulatory networks of the genome. Hence, gaining a profound understanding of how 6mA is intricately involved in these biological processes is imperative for deciphering the gene regulatory networks within organisms. In this study, we propose PSAC-6mA (Position-self-attention Capsule-6mA), a sequence-location-based self-attention capsule network. The positional layer in the model enables positional relationship extraction and independent parameter setting for each base position, avoiding parameter sharing inherent in convolutional approaches. Simultaneously, the self-attention capsule network enhances dimensionality, capturing correlation information between capsules and achieving exceptional results in feature extraction across multiple spatial dimensions within the model. Experimental results demonstrate the superior performance of PSAC-6mA in recognizing 6mA motifs across various species.
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