Harnessing the biological complexity of Big Data from LINCS gene expression signatures.

ABSTRACT: Gene expression profiling using transcriptional drug perturbations are useful for many biomedical discovery studies including drug repurposing and elucidation of drug mechanisms (MoA) and many other pharmacogenomic applications. However, limited data availability across cell types has severely hindered our capacity to progress in these areas. To fill this gap, recently, the LINCS program generated almost 1.3 million profiles for over 40,000 drug and genetic perturbations for over 70 different human cell types, including meta information about the experimental conditions and cell lines. Unfortunately, Big Data like the ones generated from the ongoing LINCS program do not enable easy insights from the data but possess considerable challenges toward their analysis. In this paper, we address s

SUBMITTER: Musa A 

PROVIDER: S-EPMC6114505 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature