Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics

Author:

Nakai Kenta,Wei Leyi

Abstract

Prediction of subcellular localization of proteins from their amino acid sequences has a long history in bioinformatics and is still actively developing, incorporating the latest advances in machine learning and proteomics. Notably, deep learning-based methods for natural language processing have made great contributions. Here, we review recent advances in the field as well as its related fields, such as subcellular proteomics and the prediction/recognition of subcellular localization from image data.

Publisher

Frontiers Media SA

Subject

General Medicine

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Protein subcellular localization prediction tools;Computational and Structural Biotechnology Journal;2024-12

2. Protein Classes Predicted by Molecular Surface Chemical Features: Machine Learning-Assisted Classification of Cytosol and Secreted Proteins;The Journal of Physical Chemistry B;2024-08-26

3. SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks;International Journal of Molecular Sciences;2024-05-16

4. MMLoc: A Multi-instance Multi-label Learning Approach for Predicting Protein Subcellular Localization from Immunofluorescence Images;2024 4th International Conference on Neural Networks, Information and Communication (NNICE);2024-01-19

5. Prediction of Protein Localization;Reference Module in Life Sciences;2024

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