ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2019
Predicting subcellular localization of multisite proteins using differently weighted multi-label k-nearest neighbors sets.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 15 citations in OpenAlex.
- A Review for Artificial Intelligence Based Protein Subcellular Localization.Biomolecules · 2024Review
- Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence.FEMS microbiology reviews · 2023Review
- Automatic classification of nerve discharge rhythms based on sparse auto-encoder and time series feature.BMC bioinformatics · 2022Article
- Multilocation proteins in organelle communication: Based on protein-protein interactions.Plant direct · 2022Article
- Prediction of RNA subcellular localization: Learning from heterogeneous data sources.iScience · 2021Review
- Effect of potassium channel noise on nerve discharge based on the Chay model.Technology and health care : official journal of the European Society for Engineering and Medicine · 2020Article
- PSO-LocBact: A Consensus Method for Optimizing Multiple Classifier Results for Predicting the Subcellular Localization of Bacterial Proteins.BioMed research international · 2019Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFor a protein to execute its function, ensuring its correct subcellular localization is essential. In addition to biological experiments, bioinformatics is widely used to predict and determine the subcellular localization of proteins. However, single-feature extraction methods cannot effectively handle the huge amount of data and multisite localization of proteins. Thus, we developed a pseudo amino acid composition (PseAAC) method and an entropy density technique to extract feature fusion information from subcellular multisite proteins.
objectivePredicting multiplex protein subcellular localization and achieve high prediction accuracy.
methodTo improve the efficiency of predicting multiplex protein subcellular localization, we used the multi-label k-nearest neighbors algorithm and assigned different weights to various attributes. The method was evaluated using several performance metrics with a dataset consisting of protein sequences with single-site and multisite subcellular localizations.
resultsEvaluation experiments showed that the proposed method significantly improves the optimal overall accuracy rate of multiplex protein subcellular localization.
conclusionThis method can help to more comprehensively predict protein subcellular localization toward better understanding protein function, thereby bridging the gap between theory and application toward improved identification and monitoring of drug targets.
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