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Computational methods in cancer gene networking

Edwin Wang¹

Correspondence: (Login to view email address)

1. Biotechnology Research Institute, National Research Council of Canada; Center for Bioinformatics, McGill University

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Document Type:

Manuscript

Date:

Received 30 December 2008 19:47 UTC; Posted 06 January 2009

Subjects:

Cancer, Genetics & Genomics, Bioinformatics

Tags:

• systems biology
• network biology
• cellular network
• Computational biology
• cancer

Abstract:

In the past few years, many high-throughput techniques have been developed and applied to biological studies. These techniques such as “next generation” genome sequencing, chip-on-chip, microarray and so on can be used to measure gene expression and gene regulatory elements in a genome-wide scale. Moreover, as these technologies become more affordable and accessible, they have become a driving force in modern biology. As a result, huge amount biological data have been produced, with the expectation of increasing number of such datasets to be generated in the future. High-throughput data are more comprehensive and unbiased, but ‘real signals’ or biological insights, molecular mechanisms and biological principles are buried in the flood of data. In current biological studies, the bottleneck is no longer a lack of data, but the lack of ingenuity and computational means to extract biological insights and principles by integrating knowledge and high-throughput data.

Here I am reviewing the concepts and principles of network biology and the computational methods which can be applied to cancer research. Furthermore, I am providing a practical guide for computational analysis of cancer gene networks.

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License:

This document is licensed to the public under the Creative Commons Attribution 3.0 License

How to cite this document:

Wang, Edwin. Computational methods in cancer gene networking . Available from Nature Precedings <http://hdl.handle.net/10101/npre.2008.2737.1> (2008)

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