A fundamental task in various disciplines of science, including biology, is to find underlying
causal relations and make use of them. Causal relations can be seen if interventions are …

Machine learning, a collection of data-analytical techniques aimed at building predictive
models from multi-dimensional datasets, is becoming integral to modern biological research …

We argue that the estimation of mutual information between high dimensional continuous
random variables can be achieved by gradient descent over neural networks. We present a …

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 …

Background Understanding the interaction among different species within a community and
their responses to environmental changes is a central goal in ecology. However, defining …

Gene co-expression networks are increasingly used to explore the system-level functionality
of genes. The network construction is conceptually straightforward: nodes represent genes …

Metabolites are the end products of cellular regulatory processes, and their levels can be
regarded as the ultimate response of biological systems to genetic or environmental …

Reconstructing gene regulatory networks from high-throughput data is a long-standing
challenge. Through the Dialogue on Reverse Engineering Assessment and Methods …

Background Elucidating gene regulatory networks is crucial for understanding normal cell
physiology and complex pathologic phenotypes. Existing computational methods for the …

We argue that the estimation of mutual information between high dimensional continuous
random variables can be achieved by gradient descent over neural networks. We present a …