identification of cancer signaling pathways from published gene expression signatures using PubLiME

A key step in RNA interference (RNAi) is assembly of the RISC, the protein-siRNA complex that mediates target RNA cleavage. Here, we show that the two strands of an siRNA duplex are not equally eligible for assembly into RISC. Rather, both the absolute and relative stabilities of the base pairs

multiple species

This tutorial covers the canonical genetic algorithm as well as more experimental forms of genetic algorithms, including parallel island models and parallel cellular genetic algorithms. The tutorial also illustrates genetic search byhyperplane sampling. The theoretical foundations of genetic

aspects of clustering, ranging from distance measures to clustering algorithms and multiple-cluster memberships. More advanced analysis aims to infer causal connections between genes directly, i.e. who is regulating whom and how. We discuss several approaches to the problem of reverse engineering

and Jordan, 2006), we show that these same algorithms can be used to solve an approximate sparse maximum likelihood problem for the binary case. We test our algorithms on synthetic data, as well as on gene expression and senate voting records data.

In addition to maintaining the GenBank nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides analysis and retrieval resources for the data in GenBank and other biological data made avail-able through the NCBI web site. NCBI resources include Entrez

Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer related cellular processes. Gene expression data is also expected to significantly aid in the development of efficient cancer diagnosis and classification platforms

We developed an approach using Bayesian networks to predict protein-protein interactions genome-wide in yeast. Our method naturally weights and combines into reliable predictions genomic features only weakly associated with interaction (e.g., mRNA co-expression, co-essentiality and co-localization). In addition to de novo predictions, it can integrate often noisy, experimental interaction datasets. We observe that at given levels of sensitivity our predictions are more accurate than the existing highthroughput experimental datasets. We validate our predictions with new TAP-tagging experiments. Our analysis, which gives a comprehensive view of yeast interactions, is available at genecensus.org/intint.

temperatures and can fuse when brought to physiological temperature. A supply of unassembled v- and t-SNAREs is needed for these intermediates to form, but not for the fusion that follows. These data imply that SNARE-pins are the minimal machinery for cellular membrane fusion.