ArticlePloS one2017
An information-based network approach for protein classification.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A review of visualisations of protein fold networks and their relationship with sequence and function.Biological reviews of the Cambridge Philosophical Society · 2023Review
- A protein structural study based on the centrality analysis of protein sequence feature networks.PloS one · 2021Article
- DCGR: feature extractions from protein sequences based on CGR via remodeling multiple information.BMC bioinformatics · 2019Article
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Authors and funding
3 authors.
Funding
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Abstract
Protein classification is one of the critical problems in bioinformatics. Early studies used geometric distances and polygenetic-tree to classify proteins. These methods use binary trees to present protein classification. In this paper, we propose a new protein classification method, whereby theories of information and networks are used to classify the multivariate relationships of proteins. In this study, protein universe is modeled as an undirected network, where proteins are classified according to their connections. Our method is unsupervised, multivariate, and alignment-free. It can be applied to the classification of both protein sequences and structures. Nine examples are used to demonstrate the efficiency of our new method.
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Registered trials
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