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Translational Biomedical Informatics

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Attention for Chapter 13: Network-Based Biomedical Data Analysis.
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Chapter title
Network-Based Biomedical Data Analysis.
Chapter number 13
Book title
Translational Biomedical Informatics
Published in
Advances in experimental medicine and biology, November 2016
DOI 10.1007/978-981-10-1503-8_13
Pubmed ID
Book ISBNs
978-9-81-101502-1, 978-9-81-101503-8
Authors

Yuxin Lin, Xuye Yuan, Bairong Shen

Editors

Bairong Shen, Haixu Tang, Xiaoqian Jiang

Abstract

Complex diseases are caused by disorders of both internal and external factors, and they account for a large proportion of human diseases. They are multigenetic and rarely a consequence of the dysfunction of single molecules. Systems biology views the living organism as an organic network. Compared with reductionism-based viewpoints, systems biology pays more attention to the interactions among molecules located at different omics levels. Based on this theory, the concepts of network biomarkers and network medicine have been proposed sequentially, which integrate clinical data with knowledge of network sciences, thereby promoting the investigation of disease pathogenesis in the era of biomedical informatics. The former aims to identify precise signals for disease diagnosis and prognosis, whereas the latter focuses on developing effective therapeutic strategies for specific patient cohorts. In this chapter, the basic concepts of systems biology and network theory are presented, and clinical applications of biomolecular networks, network biomarkers, and network medicine are then discussed.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 17 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 17 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 6 35%
Researcher 3 18%
Professor 2 12%
Student > Bachelor 2 12%
Student > Master 2 12%
Other 1 6%
Unknown 1 6%
Readers by discipline Count As %
Computer Science 8 47%
Biochemistry, Genetics and Molecular Biology 2 12%
Neuroscience 2 12%
Medicine and Dentistry 1 6%
Agricultural and Biological Sciences 1 6%
Other 0 0%
Unknown 3 18%