Cargando…
Harnessing the biological complexity of Big Data from LINCS gene expression signatures
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 hinde...
Autores principales: | Musa, Aliyu, Tripathi, Shailesh, Kandhavelu, Meenakshisundaram, Dehmer, Matthias, Emmert-Streib, Frank |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6114505/ https://www.ncbi.nlm.nih.gov/pubmed/30157183 http://dx.doi.org/10.1371/journal.pone.0201937 |
Ejemplares similares
-
L1000 Viewer: A Search Engine and Web Interface for the LINCS Data Repository
por: Musa, Aliyu, et al.
Publicado: (2019) -
Systems Pharmacogenomic Landscape of Drug Similarities from LINCS data: Drug Association Networks
por: Musa, Aliyu, et al.
Publicado: (2019) -
An Introductory Review of Deep Learning for Prediction Models With Big Data
por: Emmert-Streib, Frank, et al.
Publicado: (2020) -
Prognostic gene expression signatures of breast cancer are lacking a sensible biological meaning
por: Manjang, Kalifa, et al.
Publicado: (2021) -
Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules
por: Tripathi, Shailesh, et al.
Publicado: (2016)