Abstract：
:Data sets from recent large-scale projects can be integrated into one unified puzzle that can provide new insights into how drugs and genetic perturbations applied to human cells are linked to whole-organism phenotypes. Data that report how drugs affect the phenotype of human cell lines and how drugs induce changes in gene and protein expression in human cell lines can be combined with knowledge about human disease, side effects induced by drugs, and mouse phenotypes. Such data integration efforts can be achieved through the conversion of data from the various resources into single-node-type networks, gene-set libraries, or multipartite graphs. This approach can lead us to the identification of more relationships between genes, drugs, and phenotypes as well as benchmark computational and experimental methods. Overall, this lean 'Big Data' integration strategy will bring us closer toward the goal of realizing personalized medicine.
journal_name
Trends Pharmacol Scijournal_title
Trends in pharmacological sciencesauthors
Ma'ayan A,Rouillard AD,Clark NR,Wang Z,Duan Q,Kou Ydoi
10.1016/j.tips.2014.07.001subject
Has Abstractpub_date
2014-09-01 00:00:00pages
450-60issue
9eissn
0165-6147issn
1873-3735pii
S0165-6147(14)00112-6journal_volume
35pub_type
杂志文章abstract：:Controversy over the meaning of pharmacological parameters often arises because of a lack of appreciation of different hierarchical levels of analysis. In a recent letter in Trends in Pharmacological Sciences, Zhang and Kavana [1] concluded that my two-state model for allosterism lacks cooperativity, even though Figur...
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journal_title：Trends in pharmacological sciences
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