The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Dialogue on Reverse‐Engineering Assessment and Methods: The DREAM of High‐Throughput Pathway Inference
Abstract
Abstract: The biotechnological advances of the last decade have confronted us with an explosion of genetics, genomics, transcriptomics, proteomics, and metabolomics data. These data need to be organized and structured before they may provide a coherent biological picture. To accomplish this formidable task, the availability of an accurate map of the physical interactions in the cell that are responsible for cellular behavior and function would be exceedingly helpful, as these data are ultimately the result of such molecular interactions. However, all we have at this time is, at best, a fragmentary and only partially correct representation of the interactions between genes, their byproducts, and other cellular entities. If we want to succeed in our quest for understanding the biological whole as more than the sum of the individual parts, we need to build more comprehensive and cell‐context–specific maps of the biological interaction networks. DREAM, the Dialogue on Reverse Engineering Assessment and Methods, is fostering a concerted effort by computational and experimental biologists to understand the limitations and to enhance the strengths of the efforts to reverse engineer cellular networks from high‐throughput data. In this chapter we will discuss the salient arguments of the first DREAM conference. We will highlight both the state of the art in the field of reverse engineering as well as some of its challenges and opportunities.
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Cites
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Self-citation
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Inferring Regulatory Networks from Expression Data Using Tree-Based Methods
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“The DREAM (for ‘‘Dialogue for Reverse Engineering Assessments and Methods’’) initiative organizes an annual reverse engineering competition called the DREAM challenge [34–37].”
BackgroundWisdom of crowds for robust gene network inference
The DREAM5 Consortium, Marbach, D., Costello, J. C., Küffner, R., Vega, N. M., Prill, R. J., Camacho, D. M., Allison, K. R., Kellis, M., Collins, J. J., et al. (2012) · Nature Methods · doi:10.1038/nmeth.2016 · Cited by 1,667
Self-citationGene regulatory network inference: Data integration in dynamic models—A review
Hecker, M., Lambeck, S., Toepfer, S., van Someren, E., & Guthke, R. (2009) · Biosystems · doi:10.1016/j.biosystems.2008.12.004 · Cited by 755
Prioritizing candidate disease genes by network-based boosting of genome-wide association data
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Comparison of co-expression measures: mutual information, correlation, and model based indices
Song, L., Langfelder, P., & Horvath, S. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-328 · Cited by 460
Metadata
- Authors
- STOLOVITZKY, G., MONROE, D., & CALIFANO, A.
- Venue
- Annals of the New York Academy of Sciences
- Publisher
- Wiley
- Published
- 2007-11-16
- Volume
- 1115
- Issue
- 1
- Pages
- 1-22
- ISSN
- 0077-8923 (ISSN-L)
- 1749-6632
- OpenAlex
- W2157121418
- Cited by
- 377
- References
- 24
- FWCI
- 6.70
- Type
- journal article
- Language
- en
- Open access
- closed
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