Computational Approaches for Biomarker Discovery

Malik Yousef^1,2, Naim Najami^1,3, Loai Abedallah^2,4, Waleed Khalifa^1,2

¹The Institute of Applied Research, The Galilee Society, Shefa Amr, Israel.
²Computer Science, The College of Sakhnin, Sakhnin, Israel.
³Department of Biology, The Academic Arab College of Education, Haifa, Israel.
⁴Department of Mathematics, University of Haifa, Haifa, Israel.

DOI: 10.4236/jilsa.2014.64012                

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.

Keywords

Computational Biology, Biomarker Discovery, Machine Learning

Conflicts of Interest

The authors declare no conflicts of interest.

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