ArticlePloS one2012
Imbalanced multi-modal multi-label learning for subcellular localization prediction of human proteins with both single and multiple sites.
Article in PloS one, 2012. 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.
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- Gm-PLoc: A Subcellular Localization Model of Multi-Label Protein Based on GAN and DeepFM.Frontiers in genetics · 2022Article
- Predicting subcellular location of protein with evolution information and sequence-based deep learning.BMC bioinformatics · 2021Article
- Machine and Deep Learning for Prediction of Subcellular Localization.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Predicting human protein subcellular localization by heterogeneous and comprehensive approaches.PloS one · 2017Article
- Imbalanced multi-label learning for identifying antimicrobial peptides and their functional types.Bioinformatics (Oxford, England) · 2016Article
- Human Protein Subcellular Localization with Integrated Source and Multi-label Ensemble Classifier.Scientific reports · 2016Article
- Sparse regressions for predicting and interpreting subcellular localization of multi-label proteins.BMC bioinformatics · 2016Article
- Protein (multi-)location prediction: utilizing interdependencies via a generative model.Bioinformatics (Oxford, England) · 2015Article
- Protein (multi-)location prediction: using location inter-dependencies in a probabilistic framework.Algorithms for molecular biology : AMB · 2014Article
- HybridGO-Loc: mining hybrid features on gene ontology for predicting subcellular localization of multi-location proteins.PloS one · 2014Article
- Article
- mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines.BMC bioinformatics · 2012Article
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Authors and funding
3 authors.
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Abstract
It is well known that an important step toward understanding the functions of a protein is to determine its subcellular location. Although numerous prediction algorithms have been developed, most of them typically focused on the proteins with only one location. In recent years, researchers have begun to pay attention to the subcellular localization prediction of the proteins with multiple sites. However, almost all the existing approaches have failed to take into account the correlations among the locations caused by the proteins with multiple sites, which may be the important information for improving the prediction accuracy of the proteins with multiple sites. In this paper, a new algorithm which can effectively exploit the correlations among the locations is proposed by using gaussian process model. Besides, the algorithm also can realize optimal linear combination of various feature extraction technologies and could be robust to the imbalanced data set. Experimental results on a human protein data set show that the proposed algorithm is valid and can achieve better performance than the existing approaches.
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