Large-Scale Protein-Protein Interactions Detection by Integrating Big Biosensing Data with Computational Model

Abstract EN

Protein-protein interactions are the basis of biological functions, and studying these interactions on a molecular level is of crucial importance for understanding the functionality of a living cell.

During the past decade, biosensors have emerged as an important tool for the high-throughput identification of proteins and their interactions.

However, the high-throughput experimental methods for identifying PPIs are both time-consuming and expensive.

On the other hand, high-throughput PPI data are often associated with high false-positive and high false-negative rates.

Targeting at these problems, we propose a method for PPI detection by integrating biosensor-based PPI data with a novel computational model.

This method was developed based on the algorithm of extreme learning machine combined with a novel representation of protein sequence descriptor.

When performed on the large-scale human protein interaction dataset, the proposed method achieved 84.8% prediction accuracy with 84.08% sensitivity at the specificity of 85.53%.

We conducted more extensive experiments to compare the proposed method with the state-of-the-art techniques, support vector machine.

The achieved results demonstrate that our approach is very promising for detecting new PPIs, and it can be a helpful supplement for biosensor-based PPI data detection.