Correlation and association analyses are one of the most widely used statistical methods in
research fields, including microbiome and integrative multiomics studies. Correlation and …

This paper surveys main principles of feature selection and their recent applications in big
data bioinformatics. Instead of the commonly used categorization into filter, wrapper, and …

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 …

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 …

A comprehensive guide to the conceptual, mathematical, and implementational aspects of
analyzing electrical brain signals, including data from MEG, EEG, and LFP recordings. This …

Identifying interesting relationships between pairs of variables in large data sets is
increasingly important. Here, we present a measure of dependence for two-variable …

Background: Information on the pathophysiological differences between heart failure with
reduced ejection fraction (HFrEF) versus heart failure with preserved ejection fraction …

How should one quantify the strength of association between two random variables without
bias for relationships of a specific form? Despite its conceptual simplicity, this notion of …

Using a genome-scale, lentivirally delivered shRNA library, we performed massively parallel
pooled shRNA screens in 216 cancer cell lines to identify genes that are required for cell …

The accurate reconstruction of gene regulatory networks from large scale molecular profile
datasets represents one of the grand challenges of Systems Biology. The Algorithm for the …