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Protein-protein Interfaces

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Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble

Cheng Chen¹, Qingmei Zhang¹, Bin Yu²

¹College of Mathematics and Physics, Qingdao University of Science and Technology, Qingdao, 266061, China; Artificial Intelligence and Biomedical Big Data Research Center, Qingdao University of Science and Technology, Qingdao, 266061, China.

Computers in Biology and Medicine

|August 10, 2020

Summary

StackPPI accurately predicts protein-protein interactions (PPIs) using machine learning, improving upon existing methods for biological pathway analysis and drug design. This framework enhances understanding of cellular mechanisms by identifying significant PPI networks.

Keywords:

Multi-information fusion Protein-protein interactions Stacked ensemble classifier XGBoost

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Last Updated: Dec 12, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

A Protocol for Computer-Based Protein Structure and Function Prediction

A Protocol for Computer-Based Protein Structure and Function Prediction

Published on: November 3, 2011

Area of Science:

Computational Biology
Bioinformatics
Machine Learning in Biology

Background:

Protein-protein interactions (PPIs) are fundamental to nearly all cellular processes.
Accurate prediction of PPIs is crucial for understanding biological mechanisms and disease.
Machine learning offers promising approaches for enhancing PPI prediction accuracy.

Purpose of the Study:

To develop and evaluate StackPPI, a novel predictive framework for protein-protein interactions.
To improve the accuracy and generalization of PPI predictions using advanced machine learning techniques.
To provide a tool for inferring biologically significant PPI networks and functional pathways.

Main Methods:

Feature encoding using pseudo amino acid composition, various descriptors, and position-specific scoring matrices.
Dimensionality reduction and feature noise reduction via XGBoost.
Stacked ensemble classification using random forest, extremely randomized trees, and logistic regression algorithms in StackPPI.

Main Results:

StackPPI achieved high prediction accuracy: 89.27% ACC, 0.7859 MCC, 0.9561 AUC on Helicobacter pylori.
StackPPI achieved 94.64% ACC, 0.8934 MCC, 0.9810 AUC on Saccharomyces cerevisiae.
StackPPI demonstrated superior performance compared to state-of-the-art models on independent test sets.

Conclusions:

StackPPI is an effective tool for accurate protein-protein interaction prediction.
The framework aids in inferring biologically significant PPI networks and functional pathways.
StackPPI has potential applications in drug design and understanding complex biological mechanisms.