Ziv Bar-Joseph

TL;DR: This work determines how most of the transcriptional regulators encoded in the eukaryote Saccharomyces cerevisiae associate with genes across the genome in living cells, and identifies network motifs, the simplest units of network architecture, and demonstrates that an automated process can use motifs to assemble a transcriptional regulatory network structure.

TL;DR: The unique algorithms STEM implements to cluster and compare short time series gene expression data combined with its visualization capabilities and integration with the Gene Ontology should make STEM useful in the analysis of data from a significant portion of all microarray studies.

TL;DR: Male, but not female, transgenic mice overexpressing Sirt6 have a significantly longer lifespan than wild-type mice, and has important therapeutic implications for age-related diseases.

TL;DR: An algorithm for discovering regulatory networks of gene modules, GRAM (Genetic Regulatory Modules), that combines information from genome-wide location and expression data sets and explicitly links genes to the factors that regulate them by incorporating DNA binding data, which provide direct physical evidence of regulatory interactions.

TL;DR: The algorithm works by assigning genes to a predefined set of model profiles that capture the potential distinct patterns that can be expected from the experiment and outperforms both general clustering algorithms and algorithms designed specifically for clustering time series gene expression data.