MicroRNAs (miRNAs) are a class of short non-coding RNAs, which show tissue-specific regulatory activity on genes. Expression profiling of miRNAs is an important step for understanding the pathology of Alzheimer's disease (AD), a neurodegenerative disorder originating in the brain. Recent studies highlight that miRNAs enriched in gray matter (GM) and white matter (WM) of AD brains show different...

The objective of this study was to identify hub genes and pathways associated with hepatocellular carcinoma (HCC) by centrality analysis of a co-expression network. A co-expression network based on differentially expressed (DE) genes of HCC was constructed using the Differentially Co-expressed Genes and Links (DCGL) package. Centrality analyses, for centrality of degree, clustering coefficient,...

Network-based analysis is indispensable in analyzing high throughput biological data. Based on the assumption that the variation of gene interactions under given biological conditions could be better interpreted in the context of a large-scale and wide variety of developmental, tissue, and disease, we leverage the large quantity of publicly-available transcriptomic data > 40,000 HG U133A Affyme...

MOTIVATION Advances in molecular biological, analytical and computational technologies are enabling us to systematically investigate the complex molecular processes underlying biological systems. In particular, using high-throughput gene expression assays, we are able to measure the output of the gene regulatory network. We aim here to review datamining and modeling approaches for conceptualizi...

Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An...

We analyze the gene expression data of Zebrafish under the combined framework of complex networks and random matrix theory. The nearest neighbor spacing distribution of the corresponding matrix spectra follows random matrix predictions of Gaussian orthogonal statistics. Based on the eigenvector analysis we can divide the spectra into two parts, first part for which the eigenvector localization ...

Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar...

Biological networks can be broken down into modules, groups of interacting molecules. To uncover these functional modules and study their evolution, our research groups are developing graphtheory based strategies for the analysis of gene expression data. We are looking for groups of completely connected subgraphs (e.g. cliques) in which corresponding members have the same combination of protein...

Network-based analysis is indispensable in analyzing high-throughput biological data. Based on the assumption that the variation of gene interactions under given biological conditions could be better interpreted in the context of a large-scale and wide variety of developmental, tissue, and disease conditions, we leverage the large quantity of publicly available transcriptomic data >40,000 HG U1...

Identifying the genes that change their expressions between two conditions (such as normal versus cancer) is a crucial task that can help in understanding the causes of diseases. Differential networking has emerged as a powerful approach to detect the changes in network structures and to identify the differentially connected genes among two networks. However, existing differential network-based...