Linked Life Data

20237638

Source:http://linkedlifedata.com/resource/pubmed/id/20237638

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2010-3-18
pubmed:abstractText
Systems biology is a general trend of contemporary scientific development. When coupling the classical traditional Chinese medicine (TCM) Cold Syndrome and methodology of systems biology, we conformed to the genome, transcriptome, proteome, and metabolome that are supposed to run through the overall macro behavior, and explored the macro and micro framework of systems biology of TCM Syndrome. We introduced a new way to probe into the implicit stratification of Cold Syndrome, after surveying 4575 cases of Cold Syndrome patients and examining gene expression information of a typical Cold Syndrome pedigree by microarray. We underlined the genetic background of the Cold Syndrome family based on the molecular foundation to understand Syndrome, one of our earlier discoveries in which genes and chemical compounds in neuro-endocrine-immune (NEI) system are scored as Cold or Hot (or both) property. Results indicate that Cold Syndrome related genes play an essential role in energy metabolism, which are tightly correlated with the genes of neurotransmitters, hormones and cytokines in the NEI interaction network. Therefore, NEI interaction not only opens out mechanism of classical TCM theory on Syndrome but also enriches current research on complex diseases as well as systems biology.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1742-2051
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
6
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
613-9
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Bridging the gap between traditional Chinese medicine and systems biology: the connection of Cold Syndrome and NEI network.
pubmed:affiliation
MOE Key Laboratory of Bioinformatics and Bioinformatics Division, Tsinghua National Laboratory of Information Science and Technology/Department of Automation, Tsinghua University, Beijing 100084, China.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't