ReviewFrontiers in bioinformatics2022
Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics.
Review in Frontiers in bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- ProteinFormer: protein subcellular localization based on bioimages and modified pre-trained transformer.BMC genomics · 2025Article
- Knowledge-enhanced protein subcellular localization prediction from 3D fluorescence microscope images.Bioinformatics (Oxford, England) · 2025Article
- Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions.Proteins · 2025Article
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- Anti-Magnaporthe oryzae Activity of Streptomyces bikiniensis HD-087 In Vitro and Bioinformatics Analysis of Polyketide Synthase Gene pksL.Current microbiology · 2024Article
- Protein Classes Predicted by Molecular Surface Chemical Features: Machine Learning-Assisted Classification of Cytosol and Secreted Proteins.The journal of physical chemistry. B · 2024Article
- SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.International journal of molecular sciences · 2024Article
- Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.