Journal
GENOMICS PROTEOMICS & BIOINFORMATICS
Volume 18, Issue 5, Pages 582-592Publisher
ELSEVIER
DOI: 10.1016/j.gpb.2021.01.001
Keywords
Protein-protein interaction; Feature fusion; L1-regularized logistic regression; Gradient tree boosting; Machine learning
Categories
Funding
- National Natural Science Foundation of China [61863010]
- Key Research and Development Program of Shandong Province of China [2019GGX101001]
- Natural Science Foundation of Shandong Province of China [ZR2018MC007]
Ask authors/readers for more resources
Protein-protein interactions (PPIs) are of great importance to understand genetic mechanisms, delineate disease pathogenesis, and guide drug design. With the increase of PPI data and development of machine learning technologies, prediction and identification of PPIs have become a research hotspot in proteomics. In this study, we propose a new prediction pipeline for PPIs based on gradient tree boosting (GTB). First, the initial feature vector is extracted by fusing pseudo amino acid composition (PseAAC), pseudo position-specific scoring matrix (PsePSSM), reduced sequence and index-vectors (RSIV), and autocorrelation descriptor (AD). Second, to remove redundancy and noise, we employ L1-regularized logistic regression (L1-RLR) to select an optimal feature subset. Finally, GTB-PPI model is constructed. Five-fold cross-validation showed that GTB-PPI achieved the accuracies of 95.15% and 90.47% on Saccharomyces cerevisiae and Helicobacter pylori datasets, respectively. In addition, GTB-PPI could be applied to predict the independent test datasets for Caenorhabditis elegans, Escherichia coli, Homo sapiens, and Mus musculus, the one-core PPI network for CD9, and the crossover PPI network for the Wnt-related signaling pathways. The results show that GTB-PPI can significantly improve accuracy of PPI prediction. The code and datasets of GTB-PPI can be downloaded from https://github.com/QUST-AIBBDRC/GTB-PPI/.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available