ArticlePloS one2014
Prediction of multi-type membrane proteins in human by an integrated approach.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- iMPT-FDNPL: Identification of Membrane Protein Types with Functional Domains and a Natural Language Processing Approach.Computational and mathematical methods in medicine · 2021Article
- Predicting membrane proteins and their types by extracting various sequence features into Chou's general PseAAC.Molecular biology reports · 2018Article
- A Treatise to Computational Approaches Towards Prediction of Membrane Protein and Its Subtypes.The Journal of membrane biology · 2017Review
- Predicting the Functional Types of Singleplex and Multiplex Eukaryotic Membrane Proteins via Different Models of Chou's Pseudo Amino Acid Compositions.The Journal of membrane biology · 2016Article
- A survey of computational intelligence techniques in protein function prediction.International journal of proteomics · 2014Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Membrane proteins were found to be involved in various cellular processes performing various important functions, which are mainly associated to their types. However, it is very time-consuming and expensive for traditional biophysical methods to identify membrane protein types. Although some computational tools predicting membrane protein types have been developed, most of them can only recognize one kind of type. Therefore, they are not as effective as one membrane protein can have several types at the same time. To our knowledge, few methods handling multiple types of membrane proteins were reported. In this study, we proposed an integrated approach to predict multiple types of membrane proteins by employing sequence homology and protein-protein interaction network. As a result, the prediction accuracies reached 87.65%, 81.39% and 70.79%, respectively, by the leave-one-out test on three datasets. It outperformed the nearest neighbor algorithm adopting pseudo amino acid composition. The method is anticipated to be an alternative tool for identifying membrane protein types. New metrics for evaluating performances of methods dealing with multi-label problems were also presented. The program of the method is available upon request.
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Registered trials
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