彭司华实验室

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On March 1, 2023, Wang Shihang and Shen Zhehan published a research paper on mRNA subcellular localization prediction in Molecules
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Abstract: 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.

Link to the full text of the paper: https://www.mdpi.com/1420-3049/28/5/2284

Algorithm source code sharing address： DeepmRNALoc code released on GitHub

The deployed server address of our model ： DeepmRNALoc Web Server

DeepmRNALoc deep neural network framework

Deployed model server interface

Our vision

With the dream of exploring nature, we try to find the laws of nature in the palace of science. We firmly believe that artificial intelligence will reconstruct all fields of natural science. This is because the world we live in is nonlinear, and we can address the problem of nonlinear system modeling through deep learning. Therefore, it is our most lofty mission to promote artificial intelligence in various disciplines of scientific research.