Biological data at the omics level are highly complex, requiring powerful computational
approaches to identifying significant intrinsic characteristics to further search for informative …

Background To construct gene co-expression networks, it is necessary to evaluate the
correlation between different gene expression profiles. However, commonly used correlation …

In the past few decades, both gene expression data and protein-protein interaction (PPI)
networks have been extensively studied, due to their ability to depict important …

Gene co-expression network analysis has been shown effective in identifying functional co-
expressed gene modules associated with complex human diseases. However, existing …

Molecular networks embraced diverse biological and functional associations between
genes and gene products, which are conducive for identifying novel genes and pathways of …

Many molecular system biology approaches recognize various interactions and functional
associations of proteins that occur in cellular processing. Further understanding of the …

Background Selection of genes associated with disease plays an important role in
understanding the disease pathogenesis and discovering the therapeutic targets. Network …

Gene set enrichment analysis (GSEA) is a powerful tool to associate a disease phenotype to
a group of genes/proteins. GSEA attributes a specific weight to each gene/protein in the …

Lung cancer is one of the leading causes of cancer mortality worldwide. The main types of
lung cancer are small cell lung cancer (SCLC) and nonsmall cell lung cancer (NSCLC). In …

Weighted gene co-expression network analysis (WGCNA) is a widely used software tool that
is used to establish relationships between phenotypic traits and gene expression data. It …