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Related Experiment Video

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors

Published on: May 9, 2025

THGB: predicting ligand-receptor interactions by combining tree boosting and histogram-based gradient boosting.

Liqian Zhou¹, Jiao Song¹, Zejun Li²

¹School of Computer Science, Hunan University of Technology, Zhuzhou, 412007, Hunan, China.

Scientific Reports

|November 28, 2024

Summary

This summary is machine-generated.

THGB is a new computational model for predicting ligand-receptor interactions (LRIs), significantly improving cell-to-cell communication analysis. This method outperforms existing models in discovering novel LRIs, offering a cost-effective alternative to wet experiments.

Keywords:

Feature selection Histogram-based gradient boosting Ligand-receptor interaction Tree boosting

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A BW Reporter System for Studying Receptor-Ligand Interactions

A BW Reporter System for Studying Receptor-Ligand Interactions

Published on: January 7, 2019

Area of Science:

Computational biology
Bioinformatics
Systems biology

Background:

Ligand-receptor interactions (LRIs) are crucial for understanding cell-to-cell communication in biological and medical research.
Experimental discovery of new LRIs is often expensive and time-consuming.

Purpose of the Study:

To develop a computational model, THGB, for accurate and efficient prediction of novel ligand-receptor interactions (LRIs).
To provide a cost-effective tool for inferring cell-to-cell communication.

Main Methods:

THGB utilizes iFeature for extracting feature information from ligand-receptor (LR) pairs.
A tree boosting model is employed for selecting representative LR features.
A histogram-based gradient boosting model is used to predict high-quality LRIs.

Main Results:

THGB demonstrated superior performance compared to existing LRI prediction models (CellEnBoost, CellGiQ, CellComNet) and a protein-protein interaction model (PIPR) across six evaluation metrics.
Feature selection using the tree boosting model proved more effective than PCA, NMF, LLE, and TSVD for LRI prediction.
An ablation study confirmed that THGB with feature selection yields better results than without.

Conclusions:

THGB is a highly effective computational tool for predicting ligand-receptor interactions.
The model's feature selection strategy enhances LRI prediction accuracy.
THGB offers a valuable resource for discovering new LRIs and advancing the study of cell-to-cell communication.