Difference between revisions of "Research"

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*[[media:research_highlight_001.pdf|2008 Nature methods, Research highlight (WormNet)]]
 
*[[media:research_highlight_001.pdf|2008 Nature methods, Research highlight (WormNet)]]
 
*[[media:Gallery_NGcover_SmallVer.jpg|2008 Nature Genetics coverstory (WormNet): Network perturbation predicts phenotype]]
 
*[[media:Gallery_NGcover_SmallVer.jpg|2008 Nature Genetics coverstory (WormNet): Network perturbation predicts phenotype]]
 
=='''Collaborators'''==
 
'''Human/Animal Systems Biology'''
 
*[http://www.marcottelab.org/index.php/Main_Page Edward Marcotte, University of Texas at Austin, USA]
 
*[http://www.crg.es/ben_lehner Ben Lehner, Systems Biology Unit, EMBL-CRG, Spain]
 
*Dongryul Lee, CHA Medical Center, Korea
 
*Dongwook Han Kunkook University, Korea
 
*Jonghoon Kim, Korea University, Korea
 
 
'''Crop/Plant Systems Biology'''
 
*[http://dpb.carnegiescience.edu/labs/rhee-lab Sue Rhee, Carnegie Institution of Science, USA]
 
*[http://cropgeneticsinnovation.org/ Pamela Ronald, University of California at Davis, USA]
 
*[http://www.brutnelllab.org Thomas Brutnell, Danforth Plant Science Center, USA]
 
*Sangdong Yoo, Korea University, Korea
 
 
'''Microbial Systems Biology'''
 
*[http://www.bahnlab.com/ Yongsun Bahn, Yonsei University, Korea]
 
*Sangsun Yoon, Yonsei Medical School, Korea
 

Revision as of 20:56, 10 December 2015

Research Summary

Word Cloud from Abstraction below publications
The ultimate goal of biological research is to manipulate traits that are important for medicine, agriculture, and bio-industry. This challenging task first requires good understanding of association between genotype and phenotype. Because of high complexity of genotype as well as phenotype, complexity of the genotype-phenotype association could be even untouchable by combinatorial explosion of the number of possible associations. Therefore, modern genetics needs to be more systematic and predictive. Recently we proposed network-guided approach for genetics of complex traits. First, we construct probabilistic functional gene networks for cells or organisms by benchmarking and integrating heterogeneous multi-omics data that are in general publicly available. Then, using guilt-by-association, and other algorithms of network propagation of known biological information, we predict gene functions, phenotypic effect of loss-of-function, and epistatic interaction. The information can contribute to reconstruction of map between genotype and phenotype. The network-guided genetics method has been effectively applied for various organisms; from simple microbe yeast, to multicellular animal C. elegans, to the reference plant Arabidopsis, to the reference crop rice, and to the human.

Research Highlight

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