Network inference, which is the reconstruction of biological networks from high-throughput
data, can provide valuable information about the regulation of gene expression in cells …

Enhancers are short regulatory sequences bound by sequence-specific transcription factors
and play a major role in the spatiotemporal specificity of gene expression patterns in …

The combination of ChIP-seq and transcriptome analysis is a compelling approach to
unravel the regulation of gene expression. Several recently published methods combine …

Recently there has been an increasing interest in regression methods that deal with multiple
outputs. This has been motivated partly by frameworks like multitask learning, multisensor …

Motivation: Reconstruction of gene regulatory networks (GRNs), which explicitly represent
the causality of developmental or regulatory process, is of utmost interest and has become a …

The root epidermis of Arabidopsis provides an exceptional model for studying the molecular
basis of cell fate and differentiation. To obtain a systems-level view of root epidermal cell …

Transcript regulation is essential for cell function, and misregulation can lead to disease.
Despite technologies to survey the transcriptome, we lack a comprehensive understanding …

The inference of gene regulatory networks (GRNs) from expression data can mine the direct
regulations among genes and gain deep insights into biological processes at a network …

Motivation: High-dimensional single-cell snapshot data are becoming widespread in the
systems biology community, as a mean to understand biological processes at the cellular …

Inferring gene regulatory networks (GRNs) from microarray data can help us understand the
mechanisms of life and eventually develop effective therapies. Currently, many …