Predicting overlapping protein complexes from weighted protein interaction graphs by gradually expanding dense neighborhoods
作者：
Highlights：
• Gradually Expanding Dense Neighborhoods (GENA) is proposed for the computational prediction of protein complexes from weighted protein interaction networks.
• GENA permits the participation of proteins to multiple complexes in agreement with the underlying cell mechanisms.
• GENA outperformed three of the state of the art algorithms for predicting protein complexes in experiments with datasets from yeast and human organisms.
• Downstream analysis of the resulted clusters revealed functional homogeneity between the proteins of the same cluster.
• Significantly altered network modules were detected when GENA was applied to two co-expression networks: one generated from Parkinson patients and one from healthy individuals.
摘要
•Gradually Expanding Dense Neighborhoods (GENA) is proposed for the computational prediction of protein complexes from weighted protein interaction networks.•GENA permits the participation of proteins to multiple complexes in agreement with the underlying cell mechanisms.•GENA outperformed three of the state of the art algorithms for predicting protein complexes in experiments with datasets from yeast and human organisms.•Downstream analysis of the resulted clusters revealed functional homogeneity between the proteins of the same cluster.•Significantly altered network modules were detected when GENA was applied to two co-expression networks: one generated from Parkinson patients and one from healthy individuals.
论文关键词：Computational prediction of protein complexes,Clustering of protein–protein interaction networks,Functionally homogeneous protein clusters,Parkinson differentially expressed network modules
论文评审过程：Received 17 October 2015, Revised 30 May 2016, Accepted 30 May 2016, Available online 28 June 2016, Version of Record 21 July 2016.
论文官网地址：https://doi.org/10.1016/j.artmed.2016.05.006
