title: From signatures to models: understanding cancer using microarrays.
abstract: 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.
authors.affiliation: Center for Studies in Physics and Biology, Rockefeller University, New York, USA.
authors.forename: Eran
authors.initials: E
authors.lastname: Segal
authors.forename: Nir
authors.initials: N
authors.lastname: Friedman
authors.forename: Naftali
authors.initials: N
authors.lastname: Kaminski
authors.forename: Aviv
authors.initials: A
authors.lastname: Regev
authors.forename: Daphne
authors.initials: D
authors.lastname: Koller
mesh_terms: D000465:Algorithms; D000818:Animals; D019295:Computational Biology; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D006801:Humans; D008954:Models, Biological; D009369:Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D014158:Transcription, Genetic