School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China.
Methods Mol Biol. 2024;2822:293-309. doi: 10.1007/978-1-0716-3918-4_19.
Dynamic and reversible N-methyladenosine (mA) modifications are associated with many essential cellular functions as well as physiological and pathological phenomena. In-depth study of mA co-functional patterns in epi-transcriptomic data may help to understand its complex regulatory mechanisms. In this chapter, we describe several biclustering mining algorithms for epi-transcriptomic data to discover potential co-functional patterns. The concepts and computational methods discussed in this chapter will be particularly useful for researchers working in related fields. We also aim to introduce new deep learning techniques into the field of co-functional analysis of epi-transcriptomic data.
动态且可逆的 N6-甲基腺嘌呤（m6A）修饰与许多重要的细胞功能以及生理和病理现象有关。在 epi 转录组数据中深入研究 m6A 的共功能模式可能有助于理解其复杂的调控机制。在本章中，我们描述了几种 epi 转录组数据的双聚类挖掘算法，以发现潜在的共功能模式。本章讨论的概念和计算方法将特别有助于相关领域的研究人员。我们还旨在将新的深度学习技术引入 epi 转录组数据的共功能分析领域。
