Supervised Machine Learning Models and Protein-Protein Interaction
Network Analysis of Gene Expression Profiles Induced by Omega-3 Polyunsaturated
Fatty Acids

Sergey      Shityakov; Jane   Pei-Chen   Chang; Ching-Fang      Sun; David   Ta-Wei   Guu; Thomas      Dandekar; Kuan-Pin      Su

Abstract

Background: Omega-3 polyunsaturated fatty acids (PUFAs), such as eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids, have beneficial effects on human health, but their effect on gene expression in elderly individuals (age ≥ 65) is largely unknown. In order to examine this, the gene expression profiles were analyzed in the healthy subjects (n = 96) at baseline and after 26 weeks of supplementation with EPA+DHA to determine up-regulated and down-regulated differentially expressed genes (DEGs) triggered by PUFAs. The Protein-Protein Interaction (PPI) networks were constructed by mapping these DEGs to a human interactome and linking them to the specific pathways.

Objective: This study aimed to implement supervised machine learning models and proteinprotein interaction network analysis of gene expression profiles induced by PUFAs.

Methods: The transcriptional profile of GSE12375 was obtained from the Gene Expression Omnibus database, which is based on the Affymetrix NuGO array. The probe cell intensity data were converted into the gene expression values, and the background correction was performed by the multi-array average algorithm. The LIMMA (Linear Models for Microarray Data) algorithm was implemented to identify relevant DEGs at baseline and after 26 weeks of supplementation with a p-value < 0.05. The DAVID web server was used to identify and construct the enriched KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways. Finally, the construction of Machine Learning (ML) models, including logistic regression, naïve Bayes, and deep neural networks, were implemented for the analyzed DEGs associated with the specific pathways.

Results: The results revealed that up-regulated DEGs were associated with neurotrophin/MAPK signaling, whereas the down-regulated DEGs were linked to cancer, acute myeloid leukemia, and long-term depression pathways. Additionally, ML approaches were able to cluster the EPA/DHAtreated and control groups by the logistic regression performing the best.

Conclusion: Overall, this study highlights the pivotal changes in DEGs induced by PUFAs and provides the rationale for the implementation of ML algorithms as predictive models for this type of biomedical data.

Keywords: Supervised machine learning, gene expression, polyunsaturated fatty acids, protein-protein interaction networks, clustering, Omega-3.

Graphical Abstract

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Cite As

Current Chinese Science

Supervised Machine Learning Models and Protein-Protein Interaction Network Analysis of Gene Expression Profiles Induced by Omega-3 Polyunsaturated Fatty Acids

Abstract

Graphical Abstract