| pubmed-article:19104224 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:19104224 | lifeskim:mentions | umls-concept:C0031437 | lld:lifeskim |
| pubmed-article:19104224 | lifeskim:mentions | umls-concept:C0205210 | lld:lifeskim |
| pubmed-article:19104224 | lifeskim:mentions | umls-concept:C0205460 | lld:lifeskim |
| pubmed-article:19104224 | pubmed:issue | 1 Suppl | lld:pubmed |
| pubmed-article:19104224 | pubmed:dateCreated | 2008-12-23 | lld:pubmed |
| pubmed-article:19104224 | pubmed:abstractText | Current classification of medical diagnosis derives from observational correlation between clinical syndromes and pathologic analysis. Limited understanding of the molecular determinants of diseases encountered in the critically ill remains a major obstacle to the rationale selection of therapeutic targets. Indeed, many human diseases reflect a disorder in physiologic processes that are known to involve the interaction of many complex control loops and to respond to a variety of pharmacologic agents and environmental factors. The advent of whole-genome sequencing and other high-throughput technologies have changed biomedical research into a data-rich discipline. "Omics" data sets that describe virtually all biomolecules in the cell are now publicly available. One of the challenges faced by investigators now lies in the interpretation of vast amounts of biological data sets to derive fundamental and applied biological information about whole systems. As mechanistic understanding of disease requires more than an agglomeration of information on the expression and activities of disease-associated molecules, network analysis has been applied to biological problems. Network analysis of the biological integratome promises to identify factors that influence disease phenotype, providing unique insight into disease mechanism. Network analysis also provides a mechanistic basis for defining phenotypic differences through consideration of unique genetic and environmental factors that govern intermediate phenotypes contributing to disease expression. Lastly, network analysis offers a unique method for identifying therapeutic targets that can alter disease expression. | lld:pubmed |
| pubmed-article:19104224 | pubmed:language | eng | lld:pubmed |
| pubmed-article:19104224 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:19104224 | pubmed:citationSubset | AIM | lld:pubmed |
| pubmed-article:19104224 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:19104224 | pubmed:chemical | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:19104224 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:19104224 | pubmed:month | Jan | lld:pubmed |
| pubmed-article:19104224 | pubmed:issn | 1530-0293 | lld:pubmed |
| pubmed-article:19104224 | pubmed:author | pubmed-author:GrimaldiDavid... | lld:pubmed |
| pubmed-article:19104224 | pubmed:author | pubmed-author:ClaessensYann... | lld:pubmed |
| pubmed-article:19104224 | pubmed:author | pubmed-author:MiraJean-Paul... | lld:pubmed |
| pubmed-article:19104224 | pubmed:author | pubmed-author:ChicheJean-Da... | lld:pubmed |
| pubmed-article:19104224 | pubmed:issnType | Electronic | lld:pubmed |
| pubmed-article:19104224 | pubmed:volume | 37 | lld:pubmed |
| pubmed-article:19104224 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:19104224 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:19104224 | pubmed:pagination | S38-49 | lld:pubmed |
| pubmed-article:19104224 | pubmed:meshHeading | pubmed-meshheading:19104224... | lld:pubmed |
| pubmed-article:19104224 | pubmed:meshHeading | pubmed-meshheading:19104224... | lld:pubmed |
| pubmed-article:19104224 | pubmed:meshHeading | pubmed-meshheading:19104224... | lld:pubmed |
| pubmed-article:19104224 | pubmed:meshHeading | pubmed-meshheading:19104224... | lld:pubmed |
| pubmed-article:19104224 | pubmed:meshHeading | pubmed-meshheading:19104224... | lld:pubmed |
| pubmed-article:19104224 | pubmed:meshHeading | pubmed-meshheading:19104224... | lld:pubmed |
| pubmed-article:19104224 | pubmed:meshHeading | pubmed-meshheading:19104224... | lld:pubmed |
| pubmed-article:19104224 | pubmed:meshHeading | pubmed-meshheading:19104224... | lld:pubmed |
| pubmed-article:19104224 | pubmed:meshHeading | pubmed-meshheading:19104224... | lld:pubmed |
| pubmed-article:19104224 | pubmed:meshHeading | pubmed-meshheading:19104224... | lld:pubmed |
| pubmed-article:19104224 | pubmed:meshHeading | pubmed-meshheading:19104224... | lld:pubmed |
| pubmed-article:19104224 | pubmed:year | 2009 | lld:pubmed |
| pubmed-article:19104224 | pubmed:articleTitle | Beyond clinical phenotype: the biologic integratome. | lld:pubmed |
| pubmed-article:19104224 | pubmed:affiliation | Department of Critical Care Medicine, AP-HP, Hopital Cochin, University Paris Descartes, Paris, France. | lld:pubmed |
| pubmed-article:19104224 | pubmed:publicationType | Journal Article | lld:pubmed |
| http://linkedlifedata.com/r... | pubmed:referesTo | pubmed-article:19104224 | lld:pubmed |
