Abstract
The identification of disease-related genes and disease mechanisms is an important research goal; many studies have approached this problem by analysing genetic networks based on gene expression profiles and interaction datasets. To construct a gene network, correlations or associations among pairs of genes must be obtained. However, when gene expression data are heterogeneous with high levels of noise for samples assigned to the same condition, it is difficult to accurately determine whether a gene pair represents a significant gene–gene interaction (GGI). In order to solve this problem, we proposed a random forest-based method to classify significant GGIs from gene expression data. To train the model, we defined novel feature sets and utilised various high-confidence interactome datasets to deduce the correct answer set from known disease-specific genes. Using Alzheimer’s disease data, the proposed method showed remarkable accuracy, and the GGIs established in the analysis can be used to build a meaningful genetic network that can explain the mechanisms underlying Alzheimer’s disease.
Highlights
For a comprehensive understanding of complex disease mechanisms, network approaches are widely [1,2,3]
The most important step in the construction of a genetic interaction network is the extraction of gene–gene interactions (GGIs) from omics data profiles
The present study focused on the issue of not extracting correlated GGIs from gene expression profiles owing to heterogeneity in expression levels across samples assigned to the same
Summary
For a comprehensive understanding of complex disease mechanisms, network approaches are widely [1,2,3]. These biological networks can contain physical or genetic interactions. There are various types of genetic interaction networks with different properties, their basic role is to model relationships among molecules in order to identify and explain underlying biological processes or functional dynamics related to a disease or phenotype [4]. A novel approach for calculating the strength of interactions with significantly different correlations has been proposed [9]. Using this approach, cancer-specific gene network has been derived and it applied to classify cancer
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