Abstract
Background
The inherent correlations among gene expressions have received attention. Recently, it was reported that a set of approximately 1000 landmark genes can be utilized for prediction of expression of other genes (target genes).
Objective
The objective of this study is to predict expression values of target genes based on expression values of landmark genes.
Methods
A cluster-based regression method is proposed. In the proposed method, clusters are obtained from a set of training instances of a gene and an estimator is obtained per cluster. A test instance is assigned to one of clusters then a regression model corresponding to the cluster predicts expression value.
Results
Performance of the proposed method is measured on the GEO (Gene Expression Omnibus) expression data and the GTEx (Genotype-Tissue Expression) expression data. In terms of mean absolute error averaged across target genes, the proposed method significantly outperforms previous approaches in the case of the GEO expression data.
Conclusions
The experimental results report that the combination of clustering and regression can outperform the state-of-the art methods such as generative adversarial networks and a gradient boosting based method.
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Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1B07047156).
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H.S.S conceived the study, implemented the algorithm, analyzed the experimental result, and drafted the manuscript.
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Seok, HS. Enhancing performance of gene expression value prediction with cluster-based regression. Genes Genom 43, 1059–1064 (2021). https://doi.org/10.1007/s13258-021-01128-6
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DOI: https://doi.org/10.1007/s13258-021-01128-6