Network Medicine applies network science approaches to investigate disease
pathogenesis. Many different analytical methods have been used to infer relevant molecular …

Plants are fascinating and complex organisms. A comprehensive understanding of the
organization, function and evolution of plant genes is essential to disentangle important …

Regression models are a form of supervised learning methods that are important for
machine learning, statistics, and general data science. Despite the fact that classical …

Probability models are used for many statistical tasks, notably parameter estimation, interval
estimation, inference about model parameters, point prediction, and interval prediction …

Even though vast amounts of genome-wide gene expression data have become available in
plants, it remains a challenge to effectively mine this information for the discovery of genes …

Plant responses to environmental and intrinsic signals are tightly controlled by multiple
transcription factors (TFs). These TFs and their regulatory connections form gene regulatory …

Motivation Inferring gene regulatory networks (GRNs) from high-throughput data is an
important and challenging problem in systems biology. Although numerous GRN methods …

Gene expression measurements represent the most important source of biological data
used to unveil the interaction and functionality of genes. In this regard, several data mining …

Background Many northern-hemisphere forests are dominated by oaks. These species
extend over diverse environmental conditions and are thus interesting models for studies of …

ABSTRACT A key challenge in the analysis of longitudinal microbiome data is the inference
of temporal interactions between microbial taxa, their genes, the metabolites that they …