Literature DB >> 17925349

Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference.

Gustavo Stolovitzky1, Don Monroe, Andrea Califano.   

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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Year:  2007        PMID: 17925349     DOI: 10.1196/annals.1407.021

Source DB:  PubMed          Journal:  Ann N Y Acad Sci        ISSN: 0077-8923            Impact factor:   5.691


  139 in total

Review 1.  Advantages and limitations of current network inference methods.

Authors:  Riet De Smet; Kathleen Marchal
Journal:  Nat Rev Microbiol       Date:  2010-08-31       Impact factor: 60.633

2.  Model-based method for transcription factor target identification with limited data.

Authors:  Antti Honkela; Charles Girardot; E Hilary Gustafson; Ya-Hsin Liu; Eileen E M Furlong; Neil D Lawrence; Magnus Rattray
Journal:  Proc Natl Acad Sci U S A       Date:  2010-04-12       Impact factor: 11.205

3.  Revealing strengths and weaknesses of methods for gene network inference.

Authors:  Daniel Marbach; Robert J Prill; Thomas Schaffter; Claudio Mattiussi; Dario Floreano; Gustavo Stolovitzky
Journal:  Proc Natl Acad Sci U S A       Date:  2010-03-22       Impact factor: 11.205

4.  Algorithms for modeling global and context-specific functional relationship networks.

Authors:  Fan Zhu; Bharat Panwar; Yuanfang Guan
Journal:  Brief Bioinform       Date:  2015-08-06       Impact factor: 11.622

5.  Improving reference epigenome catalogs by computational prediction.

Authors:  Peter Ebert; Christoph Bock
Journal:  Nat Biotechnol       Date:  2015-04       Impact factor: 54.908

6.  Benchmarking regulatory network reconstruction with GRENDEL.

Authors:  Brian C Haynes; Michael R Brent
Journal:  Bioinformatics       Date:  2009-02-02       Impact factor: 6.937

Review 7.  Protein interaction predictions from diverse sources.

Authors:  Yin Liu; Inyoung Kim; Hongyu Zhao
Journal:  Drug Discov Today       Date:  2008-03-06       Impact factor: 7.851

8.  Simulating systems genetics data with SysGenSIM.

Authors:  Andrea Pinna; Nicola Soranzo; Ina Hoeschele; Alberto de la Fuente
Journal:  Bioinformatics       Date:  2011-07-06       Impact factor: 6.937

9.  Verification of systems biology research in the age of collaborative competition.

Authors:  Pablo Meyer; Leonidas G Alexopoulos; Thomas Bonk; Andrea Califano; Carolyn R Cho; Alberto de la Fuente; David de Graaf; Alexander J Hartemink; Julia Hoeng; Nikolai V Ivanov; Heinz Koeppl; Rune Linding; Daniel Marbach; Raquel Norel; Manuel C Peitsch; J Jeremy Rice; Ajay Royyuru; Frank Schacherer; Joerg Sprengel; Katrin Stolle; Dennis Vitkup; Gustavo Stolovitzky
Journal:  Nat Biotechnol       Date:  2011-09-08       Impact factor: 54.908

10.  A linear programming approach for estimating the structure of a sparse linear genetic network from transcript profiling data.

Authors:  Sahely Bhadra; Chiranjib Bhattacharyya; Nagasuma R Chandra; I Saira Mian
Journal:  Algorithms Mol Biol       Date:  2009-02-24       Impact factor: 1.405

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