Combining Sequence Entropy and Subgraph Topology for Complex Prediction in Protein Protein Interaction (PPI) Network
Background: Complex prediction from interaction network of proteins has become a
challenging task. Most of the computational approaches focus on topological structures of protein
complexes and fewer of them consider important biological information contained within amino
Objective: To capture the essence of information contained within protein sequences we have
computed sequence entropy and length. Proteins interact with each other and form different sub
Method: We integrate biological features with sub graph topological features and model complexes
by using a Logistic Model Tree.
Results: The experimental results demonstrated that our method out performs other four state-ofart
computational methods in terms of the number of detecting known protein complexes correctly.
Conclusion: In addition, our framework provides insights into future biological study and might
be helpful in predicting other types of sub graph topologies.
Journal Title: Current Bioinformatics