ArticleScientific reports2016
Large-scale identification of human protein function using topological features of interaction network.
Article in Scientific reports, 2016. 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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13 citing papers in PubMed, 12 citations in OpenAlex.
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- Comparative analyses of three complete Primula mitogenomes with insights into mitogenome size variation in Ericales.BMC genomics · 2022Article
- Computational models for prediction of protein-protein interaction in rice andFrontiers in plant science · 2022Article
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- INTEGRATIVE STATISTICAL METHODS FOR EXPOSURE MIXTURES AND HEALTH.The annals of applied statistics · 2020Article
- PS-MCL: parallel shotgun coarsened Markov clustering of protein interaction networks.BMC bioinformatics · 2019Article
- Computational identification of protein-protein interactions in model plant proteomes.Scientific reports · 2019Article
- Combining learning and constraints for genome-wide protein annotation.BMC bioinformatics · 2019Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The annotation of protein function is a vital step to elucidate the essence of life at a molecular level, and it is also meritorious in biomedical and pharmaceutical industry. Developments of sequencing technology result in constant expansion of the gap between the number of the known sequences and their functions. Therefore, it is indispensable to develop a computational method for the annotation of protein function. Herein, a novel method is proposed to identify protein function based on the weighted human protein-protein interaction network and graph theory. The network topology features with local and global information are presented to characterise proteins. The minimum redundancy maximum relevance algorithm is used to select 227 optimized feature subsets and support vector machine technique is utilized to build the prediction models. The performance of current method is assessed through 10-fold cross-validation test, and the range of accuracies is from 67.63% to 100%. Comparing with other annotation methods, the proposed way possesses a 50% improvement in the predictive accuracy. Generally, such network topology features provide insights into the relationship between protein functions and network architectures. The source code of Matlab is freely available on request from the authors.
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