Background: Self Interacting Proteins (SIPs) play an essential role in various aspects of the
structural and functional organization of the cell.
Objective: In the study, we presented a novelty sequence-based computational approach for predicting
Self-interacting proteins using Weighed-Extreme Learning Machine (WELM) model combined with an
Autocorrelation (AC) descriptor protein feature representation.
Method: The major advantage of the proposed method mainly lies in adopting an effective feature
extraction method to represent candidate self-interacting proteins by using the evolutionary information
embedded in PSI-BLAST-constructed Position Specific Scoring Matrix (PSSM); and then employing a
reliable and effective WELM classifier to perform classify.
Result: In order to evaluate the performance, the proposed approach is applied to yeast and human SIP
datasets. The experimental results show that our method obtained 93.43% and 98.15% prediction
accuracies on yeast and human dataset, respectively. Extensive experiments are carried out to compare
our approach with the SVM classifier and existing sequence-based method on yeast and human dataset.
Experimental results show that the performance of our method is better than several other state-of-theart
Conclusion: It is demonstrated that the proposed method is suitable for SIPs detection and can execute
incredibly well for identifying Sips. In order to facilitate extensive studies for future proteomics
research, we developed a freely available web server called WELM-AC-SIPs in Hypertext Preprocessor
(PHP) for predicting SIPs. The web server including source code and the datasets are available at