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 acid sequences. 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 graph topologies. Methods: 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.