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Perspective
Nature Genetics  37, S38 - S45 (2005)
doi:10.1038/ng1561

From signatures to models: understanding cancer using microarrays

Eran Segal1, Nir Friedman2, Naftali Kaminski3, Aviv Regev4 & Daphne Koller5

1  Eran Segal is at the Center for Studies in Physics and Biology, Rockefeller University, New York, USA.

2  Nir Friedman is in the School of Computer Science and Engineering, Hebrew University, Jerusalem, Israel.

3  Naftali Kaminski is at the Dorothy P. and Richard P. Simmons for Interstitial Lung Diseases, Pulmonary Allergy and Critical Care Medicine, University of Pittsburgh, USA.

4  Aviv Regev is at the Bauer Center for Genomics Research, Harvard University, Cambridge, Massachusetts, USA.

5  Daphne Koller is in the Computer Science Department, Stanford University, Stanford, California, USA.

Correspondence should be addressed to Daphne Koller koller@cs.stanford.edu
Genomics has the potential to revolutionize the diagnosis and management of cancer by offering an unprecedented comprehensive view of the molecular underpinnings of pathology. Computational analysis is essential to transform the masses of generated data into a mechanistic understanding of disease. Here we review current research aimed at uncovering the modular organization and function of transcriptional networks and responses in cancer. We first describe how methods that analyze biological processes in terms of higher-level modules can identify robust signatures of disease mechanisms. We then discuss methods that aim to identify the regulatory mechanisms underlying these modules and processes. Finally, we show how comparative analysis, combining human data with model organisms, can lead to more robust findings. We conclude by discussing the challenges of generalizing these methods from cells to tissues and the opportunities they offer to improve cancer diagnosis and management.

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Nature Genetics
ISSN: 1061-4036
EISSN: 1546-1718
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