Integrated biomedical data analysis utilizing various types of data for biomarkers identification

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Zhang, Ping
Cox, Amanda
Cripps, Allan
West, Nicholas
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Hu, XH
Shyu, CR
Bromberg, Y
Gao, J
Gong, Y
Korkin, D
Yoo, I
Zheng, JH
Date
2017
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Abstract

Biomarkers discovery research requires the integrated analyses of a variety of the data across multiple domains, including clinical data, pathology data, gene expression, epigenetic data. Proper analysis can help understand the biological mechanism and better interpret the impact of the markers to disease. Realising the nature of the data in biomedical research and translational biomedicine, we developed a data analysis pipeline with a set of computational functions and an integrated method that can serve as a template for many biomarkers discovery research. The data analysis pipeline was developed with the data collected to identify biomarkers associated with obesity related disease. The set of functions included in the analysis template were used for finding the biomarkers and their combinatorial effect associated with obesity. The functions were developed in the general way that can be extended to other study easily.

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2017 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM)
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© 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Bioinformatics and computational biology
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