Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims
to unravel the complex relationships between genes and their regulators. Deciphering these …

The problem of reconstructing nonlinear and complex dynamical systems from measured
data or time series is central to many scientific disciplines including physical, biological …

We present a consensus atlas of the human brain transcriptome in Alzheimer's disease
(AD), based on meta-analysis of differential gene expression in 2,114 postmortem samples …

While single-cell gene expression experiments present new challenges for data processing,
the cell-to-cell variability observed also reveals statistical relationships that can be used by …

One of the pressing open problems of computational systems biology is the elucidation of
the topology of genetic regulatory networks (GRNs) using high throughput genomic data, in …

Network medicine is an emerging area of research dealing with molecular and genetic
interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale …

Background Co-expression measures are often used to define networks among genes.
Mutual information (MI) is often used as a generalized correlation measure. It is not clear …

Biotechnological advances in sequencing have led to an explosion of publicly available
data via large international consortia such as The Cancer Genome Atlas (TCGA), The …

The elucidation of gene regulatory networks is one of the major challenges of systems
biology. Measurements about genes that are exploited by network inference methods are …

In the recent years, the vast amount of genetic information generated by new-generation
approaches, have led to the need of new data handling methods. The integrative analysis of …