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Research article
Supervised machine learning models and protein-protein interaction network analysis of gene expression profiles induced by omega-3 polyunsaturated fatty acids
Sergey Shityakov
Würzburg University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6953-9771
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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. 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 networks were constructed by mapping these DEGs to a human interactome and linking them to 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 the cancer, acute myeloid leukemia, and long-term depression pathways. Additionally, machine learning (ML) approaches were able to cluster the EPA/DHA-treated and control groups by the logistic regression algorithm 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
gene expression, polyunsaturated fatty acids, supervised machine learning, protein-protein interaction networks, clustering
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This is a preprint. It has not completed peer review.
Research article
Supervised machine learning models and protein-protein interaction network analysis of gene expression profiles induced by omega-3 polyunsaturated fatty acids
Sergey Shityakov
Würzburg University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-6953-9771
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 2
Posted 20 Aug, 2020
No community comments so far
No comments provided
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Bioinformatics
License:
This work is licensed under a CC BY 4.0 License. Read Full License
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. 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 networks were constructed by mapping these DEGs to a human interactome and linking them to 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 the cancer, acute myeloid leukemia, and long-term depression pathways. Additionally, machine learning (ML) approaches were able to cluster the EPA/DHA-treated and control groups by the logistic regression algorithm 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.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6