ArticleBioinformatics (Oxford, England)2010
Going from where to why--interpretable prediction of protein subcellular localization.
Article in Bioinformatics (Oxford, England), 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled it.
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Who cites it
69 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- A comprehensive benchmark of sequence-based subcellular localization predictors for human proteins.Nature methods · 2026Article
- A comprehensive review on RNA subcellular localization prediction.Briefings in functional genomics · 2026Review
- Mapping, cloning, and functional characterization of CsPBGD in leaf necrosis and its potential role in disease resistance in cucumber (Cucumis sativus L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Comprehensive analysis of the NAC transcription factor gene family in Sophora tonkinensis Gagnep.BMC plant biology · 2025Article
- Practical Applications of Language Models in Protein Sorting Prediction: SignalP 6.0, DeepLoc 2.1, and DeepLocPro 1.0.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Article
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- SCLpred-ECL: Subcellular Localization Prediction by Deep N-to-1 Convolutional Neural Networks.International journal of molecular sciences · 2024Article
- A mutation in CsGME encoding GDP-mannose 3,5-epimerase results in little and wrinkled leaf in cucumber.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2024Article
- Regulation of developmental gatekeeping and cell fate transition by the calpain protease DEK1 in Physcomitrium patens.Communications biology · 2024Article
- Protein Sorting Prediction.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Article
- PPAR-gamma agonist pioglitazone recovers mitochondrial quality control in fibroblasts fromFrontiers in pharmacology · 2023Article
- DeepLoc 2.0: multi-label subcellular localization prediction using protein language models.Nucleic acids research · 2022Article
- The grapevine aspartic protease gene family: characterization and expression modulation in response to Plasmopara viticola.Journal of plant research · 2022Article
- Genome-Wide Analyses of Aspartic Proteases on Potato Genome (Plants (Basel, Switzerland) · 2022Article
- Deciphering the Crosstalk Mechanisms of Wheat-Stem Rust Pathosystem: Genome-Scale Prediction Unravels Novel Host Targets.Frontiers in plant science · 2022Article
- A mutation in CsHY2 encoding a phytochromobilin (PΦB) synthase leads to an elongated hypocotyl 1(elh1) phenotype in cucumber (Cucumis sativus L.).TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2021Article
- Multi-responses of O-methyltransferase genes to salt stress and fiber development of Gossypium species.BMC plant biology · 2021Article
9 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationProtein subcellular localization is pivotal in understanding a protein's function. Computational prediction of subcellular localization has become a viable alternative to experimental approaches. While current machine learning-based methods yield good prediction accuracy, most of them suffer from two key problems: lack of interpretability and dealing with multiple locations.
resultsWe present YLoc, a novel method for predicting protein subcellular localization that addresses these issues. Due to its simple architecture, YLoc can identify the relevant features of a protein sequence contributing to its subcellular localization, e.g. localization signals or motifs relevant to protein sorting. We present several example applications where YLoc identifies the sequence features responsible for protein localization, and thus reveals not only to which location a protein is transported to, but also why it is transported there. YLoc also provides a confidence estimate for the prediction. Thus, the user can decide what level of error is acceptable for a prediction. Due to a probabilistic approach and the use of several thousands of dual-targeted proteins, YLoc is able to predict multiple locations per protein. YLoc was benchmarked using several independent datasets for protein subcellular localization and performs on par with other state-of-the-art predictors. Disregarding low-confidence predictions, YLoc can achieve prediction accuracies of over 90%. Moreover, we show that YLoc is able to reliably predict multiple locations and outperforms the best predictors in this area. AVAILABILITY: www.multiloc.org/YLoc.
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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.