Biology 2012, 1(1), 5-17; doi:10.3390/biology1010005

Biomarker Gene Signature Discovery Integrating Network Knowledge

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Received: 29 January 2012; in revised form: 18 February 2012 / Accepted: 21 February 2012 / Published: 27 February 2012
(This article belongs to the Special Issue Feature Papers)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract: Discovery of prognostic and diagnostic biomarker gene signatures for diseases, such as cancer, is seen as a major step towards a better personalized medicine. During the last decade various methods, mainly coming from the machine learning or statistical domain, have been proposed for that purpose. However, one important obstacle for making gene signatures a standard tool in clinical diagnosis is the typical low reproducibility of these signatures combined with the difficulty to achieve a clear biological interpretation. For that purpose in the last years there has been a growing interest in approaches that try to integrate information from molecular interaction networks. Here we review the current state of research in this field by giving an overview about so-far proposed approaches.
Keywords: biomarker; gene selection; protein-protein interactions network; personalized medicine
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MDPI and ACS Style

Cun, Y.; Fröhlich, H. Biomarker Gene Signature Discovery Integrating Network Knowledge. Biology 2012, 1, 5-17.

AMA Style

Cun Y, Fröhlich H. Biomarker Gene Signature Discovery Integrating Network Knowledge. Biology. 2012; 1(1):5-17.

Chicago/Turabian Style

Cun, Yupeng; Fröhlich, Holger. 2012. "Biomarker Gene Signature Discovery Integrating Network Knowledge." Biology 1, no. 1: 5-17.

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