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Updated: Jul 29, 2026

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MMGAT:一个图表注意力网络框架,用于ATAC-seq寻找动机.

Xiaotian Wu1,2, Wenju Hou1, Ziqi Zhao1

  • 1Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education, College of Computer Science and Technology, Jilin University, Changchun, 130012, China.

BMC bioinformatics
|April 20, 2024
PubMed
概括

这项研究介绍了MMGAT,这是一种用于识别ATAC-seq数据中的转录因子结合位点的新型图形注意力网络. MMGAT准确地发现了不同长度的多重图案,在人类和小鼠数据集上表现优于现有的工具.

关键词:
ATAC-seqq 的使用情况.同时存在的概率.图表注意力网络 图表注意力网络发现动机 发现动机在TFBS的预测预测.

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