Wenzhou University of Technology, Wenzhou, 325000, China.
Guangzhou Xinhua University, Guangzhou, 510520, China.
Comput Biol Med. 2023 Sep;163:107143. doi: 10.1016/j.compbiomed.2023.107143. Epub 2023 Jun 14.
Non-coding RNA (ncRNA) is a functional RNA molecule that plays a key role in various fundamental biological processes, such as gene regulation. Therefore, studying the connection between ncRNA and proteins holds significant importance in exploring the function of ncRNA. Although many efficient and accurate methods have been developed by modern biological scientists, accurate predictions still pose a major challenge for various issues. In our approach, we utilize a multi-head attention mechanism to merge residual connections, allowing for the automatic learning of ncRNA and protein sequence features. Specifically, the proposed method projects node features into multiple spaces based on multi-head attention mechanism, thereby obtaining different feature interaction patterns in these spaces. By stacking interaction layers, higher-order interaction modes can be derived, while still preserving the initial feature information through the residual connection. This strategy effectively leverages the sequence information of ncRNA and protein, enabling the capture of hidden high-order features. The final experimental results demonstrate the effectiveness of our method, with AUC values of 97.4%, 98.5%, and 94.8% achieved on the NPInter v2.0, RPI807, and RPI488 datasets, respectively. These impressive results solidify our method as a powerful tool for exploring the connection between ncRNAs and proteins. We have uploaded the implementation code on GitHub: https://github.com/ZZCrazy00/MHAM-NPI.
非编码 RNA（ncRNA）是一种具有关键功能的 RNA 分子，在各种基本的生物过程中发挥作用，如基因调控。因此，研究 ncRNA 与蛋白质之间的联系对于探索 ncRNA 的功能具有重要意义。尽管现代生物科学家已经开发出许多高效准确的方法，但准确预测仍然是各种问题的主要挑战。在我们的方法中，我们利用多头注意力机制融合残差连接，从而实现 ncRNA 和蛋白质序列特征的自动学习。具体来说，所提出的方法基于多头注意力机制将节点特征投影到多个空间中，从而在这些空间中获得不同的特征交互模式。通过堆叠交互层，可以得到更高阶的交互模式，同时通过残差连接保留初始特征信息。这种策略有效地利用了 ncRNA 和蛋白质的序列信息，能够捕获隐藏的高阶特征。最终的实验结果表明了我们方法的有效性，在 NPInter v2.0、RPI807 和 RPI488 数据集上分别获得了 97.4%、98.5%和 94.8%的 AUC 值。这些令人印象深刻的结果证明了我们的方法是探索 ncRNA 和蛋白质之间联系的有力工具。我们已经将实现代码上传到 GitHub：https://github.com/ZZCrazy00/MHAM-NPI。
