Biomedical subjects

T S Jaakkola

Publications and source records attributed to T S Jaakkola.

3 recordsLinked to original sources

PubMed2001

Serial regulation of transcriptional regulators in the yeast cell cycle.

Genome-wide location analysis was used to determine how the yeast cell cycle gene expression program is regulated by each of the nine known cell cycle transcriptional activators. We found that cell cycle transcriptional activators that function during one stage of the cell cycle regulate transcriptional activators that function during the next stage. This serial regulation of transcriptional activators forms a connected regulatory network that is itself a cycle. Our results also reveal how the nine transcriptional regulators coordinately regulate global gene expression and diverse stage-specific functions to produce a continuous cycle of cellular events. This information forms the foundation for a complete map of the transcriptional regulatory network that controls the cell cycle.

Cell Cycle↗

PubMed2001

Fast optimal leaf ordering for hierarchical clustering.

We present the first practical algorithm for the optimal linear leaf ordering of trees that are generated by hierarchical clustering. Hierarchical clustering has been extensively used to analyze gene expression data, and we show how optimal leaf ordering can reveal biological structure that is not observed with an existing heuristic ordering method. For a tree with n leaves, there are 2(n-1) linear orderings consistent with the structure of the tree. Our optimal leaf ordering algorithm runs in time O(n(4)), and we present further improvements that make the running time of our algorithm practical.

Algorithms↗

PubMed2001

Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks.

We propose a model-driven approach for analyzing genomic expression data that permits genetic regulatory networks to be represented in a biologically interpretable computational form. Our models permit latent variables capturing unobserved factors, describe arbitrarily complex (more than pair-wise) relationships at varying levels of refinement, and can be scored rigorously against observational data. The models that we use are based on Bayesian networks and their extensions. As a demonstration of this approach, we utilize 52 genomes worth of Affymetrix GeneChip expression data to correctly differentiate between alternative hypotheses of the galactose regulatory network in S. cerevisiae. When we extend the graph semantics to permit annotated edges, we are able to score models describing relationships at a finer degree of specification.

Bayes Theorem↗