Abstract
To achieve homeostasis, the human biological system relies on the interaction between organs through the binding of ligands secreted from source organs to receptors located on destination organs. Currently, the changing roles that receptors perform in tissues are only partially understood. Recently, a methodology based on receptor co-expression patterns to classify their tissue-specific metabolic functions was suggested. Here we present an advanced framework to predict an additional class of inflammatory receptors that use a feature space of biological pathway enrichment analysis scores of co-expression networks and their eigengene correlations. These are fed into three machine learning classifiers–eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and K-Nearest Neighbors (k-NN). We applied our methodology to subcutaneous and visceral adipose gene expression datasets derived from the GTEx (Genotype-Tissue Expression) project and compared the predictions. The XGBoost model demonstrated the best performance in predicting the pre-labeled receptors, with an accuracy of 0.89/0.8 in subcutaneous/visceral adipose. We analyzed ~700 receptors to predict eight new metabolic and 15 new inflammatory functions of receptors and four new metabolic functions for known inflammatory receptors in both adipose tissues. We cross-referenced multiple predictions using the published literature. Our results establish a picture of the changing functions of receptors for two adipose tissues that can be beneficial for drug development.
Original language | English |
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Article number | e0276699 |
Journal | PLoS ONE |
Volume | 17 |
Issue number | 10 |
DOIs | |
State | Published - Oct 2022 |
Bibliographical note
Publisher Copyright:© 2022 Yehuda, Somekh. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Keywords
- Adipose Tissue/metabolism
- Gene Expression
- Humans
- Ligands
- Subcutaneous Tissue
- Support Vector Machine