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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

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Published on: August 21, 2019

Detecting statistical interactions in immune receptor data: a comparative study.

Thomas Minotto¹, Ingrid Hobæk Haff¹, Enrico Riccardi²

¹Department of Mathematics, University of Oslo, Oslo, Norway.

Journal of Applied Statistics

|March 16, 2026

Summary

This summary is machine-generated.

Statistical interactions in immune receptor binding can be detected using machine learning. Pairwise interactions were identified with 1000 sequences, while logic regression and random forests excelled at higher-order interactions.

Keywords:

62J07 Antibody-antigen binding interaction detection logic regression logistic regression machine learning random forests

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Area of Science:

Immunology
Bioinformatics
Computational Biology

Background:

Statistical interactions are crucial in biological processes, including immune receptor-antigen binding.
Advanced machine learning methods show promise for predicting binding affinity by identifying intra-amino acid chain interactions.

Purpose of the Study:

To review and compare statistical interaction detection methods for immune receptor binding prediction.
To evaluate the performance of logistic lasso, logic regression, random forests, and neural networks in identifying simulated immune data interactions.

Main Methods:

Simulated immune data with implanted amino acid motifs determining binding status via logistic regression.
Comparison of detection performance based on interaction order, strength, frequency, and dataset size.
Evaluation of computational running times for different machine learning methods.

Main Results:

Pairwise interactions were detectable with as few as 1000 sequences, with optimal detection around 20% implantation rate.
Logic regression and random forest methods showed superior performance for higher-order interactions.
Neural network methods exhibited the fastest running times, followed by lasso-based methods.

Conclusions:

Machine learning methods effectively detect statistical interactions in immune receptor binding data.
The choice of method impacts performance based on interaction order and computational resources.
Application to experimental data identified significant pairwise and three-way interactions, improving prediction accuracy.