ArticleBMC bioinformatics2019
Protein sequence information extraction and subcellular localization prediction with gapped k-Mer method.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Core genome analysis reveals novel drug and vaccine targets in multidrug-resistant Citrobacter koseri.Molecular genetics and genomics : MGG · 2025Article
- PS3N: leveraging protein sequence-structure similarity for novel drug-drug interaction discovery.Scientific reports · 2025Article
- Review
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- ASRpro: A machine-learning computational model for identifying proteins associated with multiple abiotic stress in plants.The plant genome · 2024Article
- Dual-Signal Feature Spaces Map Protein Subcellular Locations Based on Immunohistochemistry Image and Protein Sequence.Sensors (Basel, Switzerland) · 2023Article
- Computer-aided genomic data analysis of drug-resistantFrontiers in cellular and infection microbiology · 2023Article
- PredPromoter-MF(2L): A Novel Approach of Promoter Prediction Based on Multi-source Feature Fusion and Deep Forest.Interdisciplinary sciences, computational life sciences · 2022Article
- PDAUG: a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling.BMC bioinformatics · 2022Article
- Functional Analysis of Hypothetical Proteins ofBioinformatics and biology insights · 2022Article
- Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics.Frontiers in bioinformatics · 2022Review
- Ensemble of Multiple Classifiers for Multilabel Classification of Plant Protein Subcellular Localization.Life (Basel, Switzerland) · 2021Article
- XG-PseU: an eXtreme Gradient Boosting based method for identifying pseudouridine sites.Molecular genetics and genomics : MGG · 2020Article
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSubcellular localization prediction of protein is an important component of bioinformatics, which has great importance for drug design and other applications. A multitude of computational tools for proteins subcellular location have been developed in the recent decades, however, existing methods differ in the protein sequence representation techniques and classification algorithms adopted.
resultsIn this paper, we firstly introduce two kinds of protein sequences encoding schemes: dipeptide information with space and Gapped k-mer information. Then, the Gapped k-mer calculation method which is based on quad-tree is also introduced.
conclusions>From the prediction results, this method not only reduces the dimension, but also improves the prediction precision of protein subcellular localization.
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