Comparing Functional Modules from Dynamic and Static Protein Interaction Networks
Author Information
Author(s): Tang Xiwei, Wang Jianxin, Liu Binbin, Li Min, Chen Gang, Pan Yi
Primary Institution: Central South University
Hypothesis
Dynamic protein interaction networks provide more biologically meaningful functional modules than static networks.
Conclusion
Functional modules identified from dynamic networks have significantly more biological relevance compared to those from static networks.
Supporting Evidence
- The study identified 2,063 functional modules from dynamic networks compared to 932 from static networks.
- Modules from dynamic networks showed higher specificity and sensitivity in matching known protein complexes.
- 60.26% of modules from dynamic networks were statistically significant compared to 50.73% from static networks.
Takeaway
This study shows that looking at how proteins interact over time helps scientists find important groups of proteins that work together better than just looking at them all at once.
Methodology
Time course protein interaction networks were reconstructed using gene expression data, followed by clustering to identify functional modules.
Potential Biases
Potential biases may arise from the selection of thresholds for filtering gene expression data.
Limitations
The study may not account for all possible interactions and relies on the accuracy of the gene expression data.
Participant Demographics
The study focuses on protein interactions in the yeast species S. cerevisiae.
Statistical Information
P-Value
p<0.01
Statistical Significance
p<0.01
Digital Object Identifier (DOI)
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