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Enhancing performance of gene expression value prediction with cluster-based regression.

¹Department of Computer Science and Engineering, Kangwon National University, Gangwon-do, Chuncheon-si, 24341, Korea. hsseok@kangwon.ac.kr.

Genes & Genomics

|June 28, 2021

Summary

Predicting gene expression using landmark genes is improved with a novel cluster-based regression method. This approach outperforms existing techniques on real-world gene expression datasets.

Keywords:

Clustering Gene expression value prediction Kernel ridge regression Landmark gene Performance enhancing Regression

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Area of Science:

Genomics
Bioinformatics
Machine Learning

Background:

Gene expression correlations are crucial in biological systems.
Landmark genes can predict the expression of other target genes.

Purpose of the Study:

To predict target gene expression using landmark gene expression data.
To develop and evaluate a novel computational approach for gene expression prediction.

Main Methods:

A cluster-based regression model was developed.
Clusters of gene expression data were identified from training instances.
A regression estimator was trained for each cluster.
Test instances were assigned to clusters for prediction.

Main Results:

The proposed method was evaluated on Gene Expression Omnibus (GEO) and Genotype-Tissue Expression (GTEx) datasets.
The cluster-based regression method significantly outperformed previous approaches on GEO data.
Performance was measured using mean absolute error across target genes.

Conclusions:

Combining clustering and regression enhances gene expression prediction accuracy.
The proposed method surpasses state-of-the-art techniques like generative adversarial networks and gradient boosting.
This approach offers a powerful tool for analyzing complex gene expression patterns.