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maTE: discovering expressed interactions between microRNAs and their targets
Malik Yousef1, Loai Abdallah2, Jens Allmer3,4
1Department of Community Information Systems, Zefat Academic College, Zefat, Israel.
Motivation:
Disease is often manifested via changes in transcript and protein abundance. MicroRNAs (miRNAs) are instrumental in regulating protein abundance and may measurably influence transcript levels. miRNAs often target more than one mRNA (for humans, the average is three), and mRNAs are often targeted by more than one miRNA (for the genes considered in this study, the average is also three). Therefore, it is difficult to determine the miRNAs that may cause the observed differential gene expression. We present a novel approach, maTE, which is based on machine learning, that integrates information about miRNA target genes with gene expression data. maTE depends on the availability of a sufficient amount of patient and control samples. The samples are used to train classifiers to accurately classify the samples on a per miRNA basis. Multiple high scoring miRNAs are used to build a final classifier to improve separation.
Results:
The aim of the study is to find a set of miRNAs causing the regulation of their target genes that best explains the difference between groups (e.g. cancer versus control). maTE provides a list of significant groups of genes where each group is targeted by a specific miRNA. For the datasets used in this study, maTE generally achieves an accuracy well above 80%. Also, the results show that when the accuracy is much lower (e.g. ∼50%), the set of miRNAs provided is likely not causative of the difference in expression. This new approach of integrating miRNA regulation with expression data yields powerful results and is independent of external labels and training data. Thereby, this approach allows new avenues for exploring miRNA regulation and may enable the development of miRNA-based biomarkers and drugs.
Availability And Implementation:
The KNIME workflow, implementing maTE, is available at Bioinformatics online.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
We developed maTE, a machine learning method to identify microRNAs (miRNAs) driving gene expression changes in diseases. maTE accurately pinpoints causative miRNAs, aiding in biomarker and drug development.
Area of Science:
- Genomics
- Bioinformatics
- Molecular Biology
Background:
- Disease pathogenesis involves altered transcript and protein levels.
- MicroRNAs (miRNAs) regulate protein abundance and influence transcript levels.
- Complex miRNA-mRNA interactions complicate identifying causative miRNAs for differential gene expression.
Purpose of the Study:
- To identify specific miRNAs responsible for observed gene expression differences between sample groups (e.g., disease vs. control).
- To develop a machine learning approach integrating miRNA target information with gene expression data.
Main Methods:
- A novel machine learning approach, maTE, was developed.
- maTE integrates miRNA target gene data with gene expression profiles.
- Classifiers are trained on patient and control samples to identify miRNA-specific expression patterns.
Main Results:
- maTE achieves high classification accuracy (often >80%) in identifying causative miRNAs.
- The method identifies significant gene groups targeted by specific miRNAs.
- Low accuracy suggests the identified miRNAs are unlikely to be causative.
Conclusions:
- maTE offers a powerful, data-driven approach to explore miRNA regulation.
- This method can identify miRNAs driving differential gene expression.
- maTE facilitates the development of novel miRNA-based biomarkers and therapeutics.
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