Bonn-Aachen International Center for IT (B-IT), Dahlmannstr. 2, 53113 Bonn, GermanyAbstract: 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.
How to find the robust biomarkers in the genomics data are first step to personalized medicine. Here we take a short review on how machine leaning works in find biomarkers and current aproach in this area. for more interesting technology, please see the following papers.