Guy Karlebach

TL;DR: A comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data defines the performance, data requirements and inherent biases of different inference approaches, and provides guidelines for algorithm application and development.

TL;DR: Gene regulatory networks have an important role in every process of life, including cell differentiation, metabolism, the cell cycle and signal transduction, and by understanding the dynamics of these networks the authors can shed light on the mechanisms of diseases that occur when these cellular processes are dysregulated.

TL;DR: It is shown that reconstruction of a logical GRN that minimizes the errors is NP-complete, so that an efficient exact algorithm for the problem is not likely to exist and the constructed model displays high consistency with prior biological knowledge.

TL;DR: In this article , a nonlinear similarity function is defined to map from a feature space of phenotypic abnormalities to a matrix of pairwise patient similarity that can be clustered using unsupervised machine learning.

TL;DR: An algorithm is presented that determines the smallest perturbations required for manipulating the dynamics of a network formulated as a Petri net, in order to cause or avoid a specified phenotype, and can overcome incomplete information.