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First published online April 20, 2007
Journal of Experimental Biology 210, 1507-1517 (2007)
Published by The Company of Biologists 2007
doi: 10.1242/jeb.004432

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Review Article

Extracting biology from high-dimensional biological data

John Quackenbush

Department of Biostatistics and Computational Biology and Department of Cancer Biology, Dana-Farber Cancer Institute, Boston, MA, USA and Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA

e-mail: johnq{at}jimmy.harvard.edu

Accepted 6 March 2007

The promise of the genome project was that a complete sequence would provide us with information that would transform biology and medicine. But the `parts list' that has emerged from the genome project is far from the `wiring diagram' and `circuit logic' we need to understand the link between genotype, environment and phenotype. While genomic technologies such as DNA microarrays, proteomics and metabolomics have given us new tools and new sources of data to address these problems, a number of crucial elements remain to be addressed before we can begin to close the loop and develop a predictive quantitative biology that is the stated goal of so much of current biological research, including systems biology. Our approach to this problem has largely been one of integration, bringing together a vast wealth of information to better interpret the experimental data we are generating in genomic assays and creating publicly available databases and software tools to facilitate the work of others. Recently, we have used a similar approach to trying to understand the biological networks that underlie the phenotypic responses we observe and starting us on the road to developing a predictive biology.

Key words: 'omic data analysis, microarray, bioinformatics, computational biology

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