Predicting biological networks from genomic data. Export

@article{citeulike:2580449, abstract = {Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate to high-throughput experimental methods.}, address = {Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, D-69117 Heidelberg, Germany.}, author = {Harrington, Eoghan D D. and Jensen, Lars J J. and Bork, Peer}, citeulike-article-id = {2580449}, citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.febslet.2008.02.033}, citeulike-linkout-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0014579308001415}, citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/18294967}, citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=18294967}, citeulike-linkout-4 = {http://www.sciencedirect.com/science/article/B6T36-4RWHFCC-5/2/81599d8eba7c1c5773f3b22d7b32c505}, day = {21}, doi = {10.1016/j.febslet.2008.02.033}, issn = {0014-5793}, journal = {FEBS Lett}, keywords = {community-genomics, ecological-interaction, network, software}, month = {February}, posted-at = {2008-12-05 17:52:29}, priority = {3}, title = {Predicting biological networks from genomic data.}, url = {http://dx.doi.org/10.1016/j.febslet.2008.02.033}, year = {2008} }

TY - JOUR ID - citeulike:2580449 L3 - citeulike-article-id:2580449 N2 - Continuing improvements in DNA sequencing technologies are providing us with vast amounts of genomic data from an ever-widening range of organisms. The resulting challenge for bioinformatics is to interpret this deluge of data and place it back into its biological context. Biological networks provide a conceptual framework with which we can describe part of this context, namely the different interactions that occur between the molecular components of a cell. Here, we review the computational methods available to predict biological networks from genomic sequence data and discuss how they relate to high-throughput experimental methods. JF - FEBS Lett SN - 0014-5793 AD - Structural and Computational Biology Unit, European Molecular Biology Laboratory, Meyerhofstrasse 1, D-69117 Heidelberg, Germany. TI - Predicting biological networks from genomic data. KW - community-genomics KW - ecological-interaction KW - network KW - software AU - Harrington, Eoghan D AU - Jensen, Lars J AU - Bork, Peer PY - 2008/02/21/ UR - http://dx.doi.org/10.1016/j.febslet.2008.02.033 ER -