Network deconvolution as a general method to distinguish direct dependencies in networks
Recognizing direct relationships between variables connected in a network is a pervasive
problem in biological, social and information sciences as correlation-based networks …
problem in biological, social and information sciences as correlation-based networks …
Disentangling direct from indirect relationships in association networks
Networks are vital tools for understanding and modeling interactions in complex systems in
science and engineering, and direct and indirect interactions are pervasive in all types of …
science and engineering, and direct and indirect interactions are pervasive in all types of …
Inner composition alignment for inferring directed networks from short time series
Identifying causal links (couplings) is a fundamental problem that facilitates the
understanding of emerging structures in complex networks. We propose and analyze inner …
understanding of emerging structures in complex networks. We propose and analyze inner …
Network link prediction by global silencing of indirect correlations
B Barzel, AL Barabási - Nature biotechnology, 2013 - nature.com
Predictions of physical and functional links between cellular components are often based on
correlations between experimental measurements, such as gene expression. However …
correlations between experimental measurements, such as gene expression. However …
From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data
R Opgen-Rhein, K Strimmer - BMC systems biology, 2007 - Springer
Background The use of correlation networks is widespread in the analysis of gene
expression and proteomics data, even though it is known that correlations not only confound …
expression and proteomics data, even though it is known that correlations not only confound …
Reconstructing biological networks using conditional correlation analysis
JJ Rice, Y Tu, G Stolovitzky - Bioinformatics, 2005 - academic.oup.com
Motivation: One of the present challenges in biological research is the organization of the
data originating from high-throughput technologies. One way in which this information can …
data originating from high-throughput technologies. One way in which this information can …
Network component analysis: reconstruction of regulatory signals in biological systems
High-dimensional data sets generated by high-throughput technologies, such as DNA
microarray, are often the outputs of complex networked systems driven by hidden regulatory …
microarray, are often the outputs of complex networked systems driven by hidden regulatory …
Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks
A Reverter, EKF Chan - Bioinformatics, 2008 - academic.oup.com
Motivation: We present PCIT, an algorithm for the reconstruction of gene co-expression
networks (GCN) that combines the concept partial correlation coefficient with information …
networks (GCN) that combines the concept partial correlation coefficient with information …
Integrating gene regulatory pathways into differential network analysis of gene expression data
The advent of next-generation sequencing has introduced new opportunities in analyzing
gene expression data. Research in systems biology has taken advantage of these …
gene expression data. Research in systems biology has taken advantage of these …
Integrative analysis of many weighted co-expression networks using tensor computation
W Li, CC Liu, T Zhang, H Li… - PLoS computational …, 2011 - journals.plos.org
The rapid accumulation of biological networks poses new challenges and calls for powerful
integrative analysis tools. Most existing methods capable of simultaneously analyzing a …
integrative analysis tools. Most existing methods capable of simultaneously analyzing a …