Ron Shamir

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: This paper defines an appropriate stochastic error model on the input, and proves that under the conditions of the model, the algorithm recovers the cluster structure with high probability, and presents a practical heuristic based on the same algorithmic ideas.

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: The current computational approaches for theFunctional annotation of proteins are described, including direct methods, which propagate functional information through the network, and module‐assisted methods, who infer functional modules within the network and use those for the annotation task.

TL;DR: A new method to detect significant biclusters in large expression datasets is proposed and is able to detect and relate finer tissue types than was previously possible in cancer data and outperforms the biclustering algorithm of Cheng and Church (2000).