Abstract
Gene regulatory networks are powerful abstractions of biological systems. Since the advent of high-throughput measurement technologies in biology in the late 1990s, reconstructing the structure of such networks has been a central computational problem in systems biology. While the problem is certainly not solved in its entirety, considerable progress has been made in the last two decades, with mature tools now available. This chapter aims to provide an introduction to the basic concepts underpinning network inference tools, attempting a categorization which highlights commonalities and relative strengths. While the chapter is meant to be self-contained, the material presented should provide a useful background to the later, more specialized chapters of this book.
| Original language | English |
|---|---|
| Title of host publication | Gene Regulatory Networks: Methods and Protocols |
| Editors | Guido Sanguinetti, Vân Anh Huynh-Thu |
| Place of Publication | New York, NY |
| Publisher | Springer |
| Pages | 1-23 |
| Number of pages | 23 |
| ISBN (Electronic) | 978-1-4939-8882-2 |
| ISBN (Print) | 978-1-4939-8881-5 |
| DOIs | |
| Publication status | Published - 2019 |
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Gene Regulatory Networks: Methods and Protocols
Sanguinetti, G. (Editor) & Huynh-Thu, V. A. (Editor), 2019, Humana Press. 285 p. (Methods in Molecular Biology Series Volume; vol. 1883)Research output: Book/Report › Book
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Tree-Based Learning of Regulatory Network Topologies and Dynamics with Jump3
Huynh-Thu, V. A. & Sanguinetti, G., 2019, Gene Regulatory Networks: Methods and Protocols. Sanguinetti, G. & Huynh-Thu, V. A. (eds.). New York, NY: Springer, p. 217-233 17 p.Research output: Chapter in Book/Report/Conference proceeding › Chapter
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