Understanding diseases requires identifying the differences between healthy and affected
tissues. Gene expression data have revolutionized the study of diseases by making it …

Big data is transforming biomedical research by integrating massive amounts of data from
laboratory experiments, clinical investigations, healthcare records, and the internet of things …

Gene co-expression networks can be used to associate genes of unknown function with
biological processes, to prioritize candidate disease genes or to discern transcriptional …

In the high-dimensional setting, this article considers three interrelated problems:(a) testing
the equality of two covariance matrices and;(b) recovering the support of; and (c) testing the …

Background Besides its well-established role in nerve cell survival and adaptive plasticity,
brain-derived neurotrophic factor (BDNF) is also involved in energy homeostasis and …

Transcription factor (TF) regulation is often post-translational. TF modifications such as
reversible phosphorylation and missense mutations, which can act independent of TF …

High-content, high-density long or short oligonucleotide microarrays for simultaneous
measurement of redundancy of RNA species are nowadays widely used for hypothesis …

Background To understand the roles they play in complex diseases, genes need to be
investigated in the networks they are involved in. Integration of gene expression and …

Background Elucidation of regulatory networks, including identification of regulatory
mechanisms specific to a given biological context, is a key aim in systems biology. This has …

Unveiling functional relationships between various molecular cell phenotypes from data
using machine learning models is a key promise of multiomics. Existing methods either use …