title: Large-scale protein-protein interactions detection by integrating big biosensing data with computational model.
abstract: 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.
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China.
authors.forename: Zhu-Hong
authors.initials: ZH
authors.lastname: You
authors.affiliation: Department of Computing, Hong Kong Polytechnic University, Hong Kong.
authors.forename: Shuai
authors.initials: S
authors.lastname: Li
authors.affiliation: Department of Medical Imaging, Suzhou Institute of Biomedical Engineering and Technology, Suzhou, Jiangsu 215163, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Gao
authors.affiliation: Department of Computing, Hong Kong Polytechnic University, Hong Kong.
authors.forename: Xin
authors.initials: X
authors.lastname: Luo
authors.affiliation: College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong 518060, China.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Ji
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015374:Biosensing Techniques; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D057166:High-Throughput Screening Assays; D006801:Humans; D025941:Protein Interaction Mapping / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity