Self-Interacting Proteins (SIPs), whose two or more copies can interact with each other,
have significant roles in cellular functions and evolution of Protein Interaction Networks (PINs).
Knowing whether a protein can act on itself is important to understand its functions. Previous
studies on SIPs have focused on their structures and functions, while their whole properties are less
emphasized. Not surprisingly, identifying SIPs is one of the most important works in biomedical
research, which will help to understanding the function and mechanism of proteins. It is worth
noting that high throughput methods can be used for SIPs prediction, but can be costly, time
consuming and challenging. Therefore, it is urgent to design computational models for the
identification of SIPs. In this review, the concept and function of SIPs were introduced in detail.
We further introduced SIPs data and some excellent computational models that have been designed
for SIPs prediction. Specially, the most existing approaches were developed based on machine
learning through carrying out different extract feature methods. Finally, we discussed several
difficult problems in developing computational models for SIPs prediction.
Keywords: Self-interacting proteins, computational models, protein interaction, machine learning, biomedical research, cellular
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