Background：
Protein–protein interactions (PPIs) are involved in a number of cellular processes and play a key role inside cells. The prediction of PPIs is an important task towards the understanding of many bioinformatics functions and applications, such as predicting protein functions, gene-disease associations and disease-drug associations. Given that high-throughput methods are expensive and time-consuming, it is a challenging task to develop efficient and accurate computational methods for predicting PPIs .
Results：
In the study, a novel computational approach named WELM-SURF was developed to predict PPIs. The proposed method used Position Specific Scoring Matrix (PSSM) to capture protein evolutionary information and employed Speed Up Robot Features (SURF) to extract key features from PSSM of protein sequence. Weighted Extreme Learning Machine (WELM) is featured with short training time and great ability to execute classification efficiently by optimizing the loss function of weight matrix. Therefore, WELM classifier was used to carry out classification. The cross-validation results show that WELM-SURF obtains 97.36% and 95.12% of average accuracy on yeast and human dataset, respectively. The prediction ability of WELM-SURF was also compared with those of ELM-SRUF, SVM-SURF and other existing approaches. The comparison results further verify that WELM-SURF is obviously better than other methods.
Conclusion：
The experimental results proved that the WELM-SURF method is very useful for predicting PPIs and can also be applied to other bioinformatics studies of protein.