Combining SVM and ECOC for Identification of Protein Complexes from Protein Protein Interaction Networks by Integrating Amino Acids’ Physical Properties and Complex Topology

Faridoon, Amen; Sikandar, Aisha; Imran, Muhammad; Ghouri, Saman; Sikandar, Misba; Sikandar, Waseem

doi:10.1007/s12539-020-00369-5

Cited by 4 publications

(1 citation statement)

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“…Finally, the model is obtained by second-order approximation and used to identify protein complexes. Faridoon [ 18 ] et al combined the support vector machine with the ECOC algorithm. In addition, the physical properties of amino acids and various topological information are used as features to predict protein complexes from the PPI network.…”

Section: Introductionmentioning

confidence: 99%

A supervised protein complex prediction method with network representation learning and gene ontology knowledge

et al. 2022

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Background Protein complexes are essential for biologists to understand cell organization and function effectively. In recent years, predicting complexes from protein–protein interaction (PPI) networks through computational methods is one of the current research hotspots. Many methods for protein complex prediction have been proposed. However, how to use the information of known protein complexes is still a fundamental problem that needs to be solved urgently in predicting protein complexes. Results To solve these problems, we propose a supervised learning method based on network representation learning and gene ontology knowledge, which can fully use the information of known protein complexes to predict new protein complexes. This method first constructs a weighted PPI network based on gene ontology knowledge and topology information, reducing the network's noise problem. On this basis, the topological information of known protein complexes is extracted as features, and the supervised learning model SVCC is obtained according to the feature training. At the same time, the SVCC model is used to predict candidate protein complexes from the protein interaction network. Then, we use the network representation learning method to obtain the vector representation of the protein complex and train the random forest model. Finally, we use the random forest model to classify the candidate protein complexes to obtain the final predicted protein complexes. We evaluate the performance of the proposed method on two publicly PPI data sets. Conclusions Experimental results show that our method can effectively improve the performance of protein complex recognition compared with existing methods. In addition, we also analyze the biological significance of protein complexes predicted by our method and other methods. The results show that the protein complexes predicted by our method have high biological significance.

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Section: Introductionmentioning

confidence: 99%