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Causal reasoning on biological networks: interpreting transcriptional changes
TLDR
A simple scoring function can discriminate between a large number of competing molecular hypotheses about the upstream cause of the changes observed in a gene expression profile. Expand
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Precompetitive activity to address the biological data needs of drug discovery
The efficiency and effectiveness of target selection and validation could be improved with accessible, standardized and integrated biological reference data sets. Such resources should be establishedExpand
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GECKO: a complete large-scale gene expression analysis platform
TLDR
The Gecko framework is very general: non-Affymetrix and non-gene expression data can be analyzed as well. Expand
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Robust phenotype prediction from gene expression data using differential shrinkage of co-regulated genes
TLDR
We integrate biological regulatory networks into a weighted group-lasso model and differentially weights gene sets based on inferred active regulatory mechanism. Expand
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A Correlated Noise Model for the Signiflcance Analysis of Gene Expression Data
Motivation: The Student t-test, applied to two-tissue comparisons based on Affymetrix chip data, often results in the non-operational conundrum of “all genes have significantly different regulation”,Expand
Causal Reasoning on Biological Networks: Interpreting Transcriptional Changes - (Extended Abstract)
TLDR
Over the past decade gene expression data sets have been generated at an increasing pace. Expand
Network-Driven Analysis Methods and their Application to Drug Discovery
TLDR
This chapter will present some of the problems facing the pharmaceutical industry and elaborate on the current state of network-driven analysis methods. Expand
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