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Open Access Highly Accessed Editorial

Cancer computational biology

Zohar Yakhini12 and Igor Jurisica345*

Author affiliations

1 Agilent Laboratories, Tel-Aviv, Israel

2 Computer Science Department, Technion, Haifa, Israel

3 Ontario Cancer Institute, PMH/UHN and the Campbell Family Institute for Cancer Research, IBM Life Sciences Discovery Centre, Toronto, Ontario, Canada

4 Department of Computer Science, University of Toronto, Toronto, Ontario, Canada

5 Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada

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Citation and License

BMC Bioinformatics 2011, 12:120  doi:10.1186/1471-2105-12-120

Published: 26 April 2011

First paragraph (this article has no abstract)

Introduction of high-throughput measurement technologies combined with the increase of the scientific knowledge base, with respect to our understanding of cellular and biological processes, resulted in establishing computer and information science as an important and fundamental component of modern biology. High-throughput measurement technologies, such as microarray-based profiling, mass spectrometry screens, and high-throughput sequencing, give rise to several computational challenges. On one hand, they require a rigorous approach to assay design. Scientists and technology developers work on optimizing assay components so as to maximize the information obtained through the measurement. On the other hand, the use of high-throughput measurement gives rise to large quantities of data that needs to be pre-processed and analyzed to obtain meaningful knowledge. This processing and analysis is performed on various levels - from pre-processing the raw data, such as images from microarrays or raw sequence reads - to analyzing the data and to the discovery of biomarkers or other biologically meaningful characteristics. Measurement technology addresses several aspects of cellular processes such as DNA, RNA, proteomics, metabolomics, epigenetics and pathways. This increase in the scientific knowledge base also leads to a central role played by data analysis and modeling, strongly grounded in computational methods. Systems biology or integrative biology approaches and network analysis are of specific importance in this context.