School of Information Engineering, Huzhou University, Huzhou 313000, China.
Shanghai Institute for Advanced Immunochemical Studies, School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.
Molecules. 2023 Mar 1;28(5):2284. doi: 10.3390/molecules28052284.
The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA's subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. In this study, a deep neural network-based eukaryotic mRNA subcellular location prediction method, DeepmRNALoc, was proposed, utilizing a two-stage feature extraction strategy that featured bimodal information splitting and fusing for the first stage and a VGGNet-like CNN module for the second stage. The five-fold cross-validation accuracies of DeepmRNALoc in the cytoplasm, endoplasmic reticulum, extracellular region, mitochondria, and nucleus were 0.895, 0.594, 0.308, 0.944, and 0.865, respectively, demonstrating that it outperforms existing models and techniques.
信使 RNA（mRNA）的亚细胞定位精确控制着蛋白质产物在哪里合成以及在哪里发挥作用。然而，通过湿实验获得 mRNA 的亚细胞定位既耗时又昂贵，并且许多现有的 mRNA 亚细胞定位预测算法需要改进。在这项研究中，提出了一种基于深度神经网络的真核 mRNA 亚细胞位置预测方法 DeepmRNALoc，该方法利用了两阶段特征提取策略，该策略的特点是第一阶段的双峰信息分割和融合，以及第二阶段的 VGGNet 样 CNN 模块。DeepmRNALoc 在细胞质、内质网、细胞外区、线粒体和细胞核中的五重交叉验证准确率分别为 0.895、0.594、0.308、0.944 和 0.865，表明它优于现有模型和技术。
