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Zhang, X.G., Lu, X., Shi, Q., Xu, X.Q., Leung, H.C.E., Harris, L.N., et al. (2006) Recursive SVM Feature Selection and Sample Classification for Mass-Spectrometry and Microarray Data. BMC Bioinformatics, 7, 197.
http://dx.doi.org/10.1186/1471-2105-7-197

has been cited by the following article:

• TITLE: Computational Approaches for Biomarker Discovery

  AUTHORS: Malik Yousef, Naim Najami, Loai Abedallah, Waleed Khalifa

  KEYWORDS: Computational Biology, Biomarker Discovery, Machine Learning

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.6 No.4, October 15, 2014

  ABSTRACT: Computational biology plays a significant role in the discovery of new biomarkers, the analyses of disease states and the validation of potential biomarkers. Biomarkers are used to measure the progress of disease or the physiological effects of therapeutic intervention in the treatment of disease. They are also used as early warning signs for various diseases such as cancer and inflammatory diseases. In this review, we outline recent progresses of computational biology application in research on biomarkers discovery. A brief discussion of some necessary preliminaries on machine learning techniques (e.g., clustering and support vector machines—SVM) which are commonly used in many applications to biomarkers discovery is given and followed by a description of biological background on biomarkers. We further examine the integration of computational biology approaches and biomarkers. Finally, we conclude with a discussion of key challenges for computational biology to biomarkers discovery.