Journal
COMPUTERS IN BIOLOGY AND MEDICINE
Volume 123, Issue -, Pages -Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compbiomed.2020.103899
Keywords
Protein-protein interactions; Multi-information fusion; XGBoost; Stacked ensemble classifier
Categories
Funding
- National Nature 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, ZR2019MEE066]
Ask authors/readers for more resources
Protein-protein interactions (PPIs) are involved with most cellular activities at the proteomic level, making the study of PPIs necessary to comprehending any biological process. Machine learning approaches have been explored, leading to more accurate and generalized PPIs predictions. In this paper, we propose a predictive framework called StackPPI. First, we use pseudo amino acid composition, Moreau-Broto, Moran and Geary autocorrelation descriptor, amino acid composition position-specific scoring matrix, Bi-gram position-specific scoring matrix and composition, transition and distribution to encode biologically relevant features. Secondly, we employ XGBoost to reduce feature noise and perform dimensionality reduction through gradient boosting and average gain. Finally, the optimized features that result are analyzed by StackPPI, a PPIs predictor we have developed from a stacked ensemble classifier consisting of random forest, extremely randomized trees and logistic regression algorithms. Five-fold cross-validation shows StackPPI can successfully predict PPIs with an ACC of 89.27%, MCC of 0.7859, AUC of 0.9561 on Helicobacter pylori, and with an ACC of 94.64%, MCC of 0.8934, AUC of 0.9810 on Saccharomyces cerevisiae. We find StackPPI improves protein interaction prediction accuracy on independent test sets compared to the state-of-the-art models. Finally, we highlight StackPPI's ability to infer biologically significant PPI networks. StackPPI's accurate prediction of functional pathways make it the logical choice for studying the underlying mechanism of PPIs, especially as it applies to drug design.
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