ArticleBriefings in bioinformatics2025
Deep generative model for protein subcellular localization prediction.
Article in Briefings in bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Characterization of BrSTM16 from Brassica rapa and functional analysis of its role in regulating cold tolerance.BMC plant biology · 2026Article
- A multi-label cascade flexible neural forest model for predicting the subcellular location of multi-site bacterial proteins.Scientific reports · 2026Article
- A 2025 meeting report from the Venice lagoon: International graduate programRNA (New York, N.Y.) · 2026Article
- Advances and opportunities for computational interrogation of plant proteins.The Plant journal : for cell and molecular biology · 2026Review
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- A comprehensive study based on large-sample multi-omics integration and machine learning to decode mitochondria-associated genes: from digestive tract tumours to gastric cancer.Frontiers in immunology · 2026Article
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Authors and funding
9 authors.
Funding
Abstract
Protein sequence not only determines its structure but also provides important clues of its subcellular localization. Although a series of artificial intelligence models have been reported to predict protein subcellular localization, most of them provide only textual outputs. Here, we present deepGPS, a deep generative model for protein subcellular localization prediction. After training with protein primary sequences and fluorescence images, deepGPS shows the ability to predict cytoplasmic and nuclear localizations by reporting both textual labels and generative images as outputs. In addition, cell-type-specific deepGPS models can be developed by using distinct image datasets from different cell lines for comparative analyses. Moreover, deepGPS shows potential to be further extended for other specific organelles, such as vesicles and endoplasmic reticulum, even with limited volumes of training data. Finally, the openGPS website (https://bits.fudan.edu.cn/opengps) is constructed to provide a publicly accessible and user-friendly platform for studying protein subcellular localization and function.
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Registered trials
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