ArticlePloS one2014
Predicting human protein subcellular locations by the ensemble of multiple predictors via protein-protein interaction network with edge clustering coefficients.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Graph algorithms for predicting subcellular localization at the pathway levelPacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Article
- DPPN-SVM: Computational Identification of Mis-Localized Proteins in Cancers by Integrating Differential Gene Expressions With Dynamic Protein-Protein Interaction Networks.Frontiers in genetics · 2020Article
- ACP-DL: A Deep Learning Long Short-Term Memory Model to Predict Anticancer Peptides Using High-Efficiency Feature Representation.Molecular therapy. Nucleic acids · 2019Article
- PMLPR: A novel method for predicting subcellular localization based on recommender systems.Scientific reports · 2018Article
- UltraPse: A Universal and Extensible Software Platform for Representing Biological Sequences.International journal of molecular sciences · 2017Article
- Predicting Protein Submitochondrial Locations: The 10th Anniversary.Current genomics · 2017Review
- Accurate prediction of functional effects for variants by combining gradient tree boosting with optimal neighborhood properties.PloS one · 2017Article
- Learning from Heterogeneous Data Sources: An Application in Spatial Proteomics.PLoS computational biology · 2016Article
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Authors and funding
2 authors.
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Abstract
One of the fundamental tasks in biology is to identify the functions of all proteins to reveal the primary machinery of a cell. Knowledge of the subcellular locations of proteins will provide key hints to reveal their functions and to understand the intricate pathways that regulate biological processes at the cellular level. Protein subcellular location prediction has been extensively studied in the past two decades. A lot of methods have been developed based on protein primary sequences as well as protein-protein interaction network. In this paper, we propose to use the protein-protein interaction network as an infrastructure to integrate existing sequence based predictors. When predicting the subcellular locations of a given protein, not only the protein itself, but also all its interacting partners were considered. Unlike existing methods, our method requires neither the comprehensive knowledge of the protein-protein interaction network nor the experimentally annotated subcellular locations of most proteins in the protein-protein interaction network. Besides, our method can be used as a framework to integrate multiple predictors. Our method achieved 56% on human proteome in absolute-true rate, which is higher than the state-of-the-art methods.
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