Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 90s, reconstructing the structure of such networks has been a central computational problem in systems biology. While the problem is certainly not solved in its entirety, considerable progress has been made in the last two decades, with mature tools now available. This chapter aims to provide an introduction to the basic concepts underpinning network inference tools, attempting a categorisation which highlights commonalities and relative strengths. While the chapter is meant to be self-contained, the material presented should provide a useful background to the later, more specialised c...
Abstract In this chapter, we study different gene regulatory network learning methods based on penal...
International audienceBACKGROUND: Reverse engineering in systems biology entails inference of gene r...
Inferring comprehensive regulatory networks from high-throughput data is one of the foremost challen...
This volume explores recent techniques for the computational inference of gene regulatory networks (...
Transcriptomic data quantifying gene expression states for single cells or cell populations at a gen...
Transcriptomic data quantifying gene expression states for single cells or cell populations at a gen...
To understand how the components of a complex system like the biological cell interact and regulate ...
In this paper, we present a systematic and conceptual overview of methods for inferring gene regulat...
(A) Our network inference algorithm takes as input a gene expression matrix, X, and a prior on netwo...
Gene regulatory networks inferred from RNA abundance data have generated significant interest, but d...
Background: Altered networks of gene regulation underlie many complex conditions, including cancer. ...
Numerous methods have been developed for inferring gene regulatory networks from expression data, ho...
Advances in molecular biological and computational technologies are enabling us to systematically in...
Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of l...
The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intric...
Abstract In this chapter, we study different gene regulatory network learning methods based on penal...
International audienceBACKGROUND: Reverse engineering in systems biology entails inference of gene r...
Inferring comprehensive regulatory networks from high-throughput data is one of the foremost challen...
This volume explores recent techniques for the computational inference of gene regulatory networks (...
Transcriptomic data quantifying gene expression states for single cells or cell populations at a gen...
Transcriptomic data quantifying gene expression states for single cells or cell populations at a gen...
To understand how the components of a complex system like the biological cell interact and regulate ...
In this paper, we present a systematic and conceptual overview of methods for inferring gene regulat...
(A) Our network inference algorithm takes as input a gene expression matrix, X, and a prior on netwo...
Gene regulatory networks inferred from RNA abundance data have generated significant interest, but d...
Background: Altered networks of gene regulation underlie many complex conditions, including cancer. ...
Numerous methods have been developed for inferring gene regulatory networks from expression data, ho...
Advances in molecular biological and computational technologies are enabling us to systematically in...
Transcriptional regulation is a fundamental molecular mechanism involved in almost every aspect of l...
The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intric...
Abstract In this chapter, we study different gene regulatory network learning methods based on penal...
International audienceBACKGROUND: Reverse engineering in systems biology entails inference of gene r...
Inferring comprehensive regulatory networks from high-throughput data is one of the foremost challen...