Abstract
Networks representing biomedical data have become a powerful approach in different research disciplines dealing with complex diseases. Also, R and Bioconductor have emerged as a standard research environment to investigate and analyze high-throughput data. Therefore, we present and discuss existing packages, available in R or Bioconductor, that provide methods for computational diagnostics by means of networks. In particular, we summarize packages to reconstruct and analyze networks from high-throughput data. Moreover, we discuss packages that provide comprehensive methods to visualize large-scale gene networks in order to support the field of computational diagnostics of complex diseases. The aim of this chapter is to support an interdisciplinary research community dealing with computational diagnostics to investigate novel hypothesis in a medical and clinical context to gain a better understanding of complex diseases.
Original language | English |
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Title of host publication | Computational Medicine: Tools and Challenges |
Publisher | Springer-Verlag Wien |
Pages | 185-197 |
Number of pages | 13 |
ISBN (Print) | 9783709109472, 3709109469, 9783709109465 |
DOIs | |
Publication status | Published - 1 Mar 2014 |
Publication type | A3 Book chapter |
ASJC Scopus subject areas
- General Biochemistry,Genetics and Molecular Biology
- General Medicine