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MTopGo is a novel algorithm of module identification for PPI Network analysis, it is designed to consider two key aspects of these models, the topological properties of the network and the apriori knowledge about the proteins involved, represented by GO annotations.
MTopGO rely on random components, thus stability of the results across different runs is a critical aspect of the algorithm. Moreover, when evaluating an algorithm specific for PPI Networks an important aspect is the stability in presence of false positive and false negative edges. In this work, two different stability analyses have been executed to evaluate MTopGO performance. Firstly, one to evaluate the stability of the result over many runs starting from a same input, to consider the range of variability introduced by the random components of the algorithm; secondly, one to evaluate the robustness of the output clusters when the input is affected by noise and uncertainty.
The results showed that MTopGO was more stable in case of false negative edges than false positive edges (adding false edges to a PPI Network was more damaging than removing existing links).
This is an abstract which has been accepted for the NETTAB 2017 Workshop