A review of connectivity map and computational approaches in pharmacogenomics
By A. Musa, L. S. Ghoraie, Shu-Dong Zhang, Galina V. Glazko, O. Yli-Harja, Matthias Dehmer, Benjamin Haibe-Kains, Frank Emmert-Streib. 2017
251 citations recorded by the source corpus.
Subfields: Computational Theory and Mathematics, Molecular Biology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Theory and Mathematics
- Kathleen M. Jagodnik
- Emilie Kaufmann
- Stephan C. Schürer
- Amar Koleti
- Dusica Vidovic
- Jaroslaw Meller
- Xiaoxi Zeng
In Molecular Biology
- Siva Sivaganesan
- Mario Medvedovic
- Gordon B. Mills
- Joe W. Gray
- Alexander Lachmann
- James Korkola
- Stephan C. Schürer
- Leslie M. Thompson
Citations in the corpus
- Machine learning applications in drug development (2019, background): “…of the drug development pipeline – for example, drug discovery through high-throughput screening (HTS) of drugs via genomic and transcriptomic data [29], genome-wide association studies (GWAS) to uncover new relevant drug targets [30], and increasing application of generic algorithms from machine…”
- The Library of Integrated Network-based Cellular Signatures (LINCS) NIH Program: System-level Cataloging of Human Cells Response to Perturbations (2017, background): “The Query app enables users to find positive and negative connections between a user gene signature and all signatures in CMAP.”
- HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine (2020, background): “The recent explosion of availability of such transcriptomics perturbation datasets has transformed the field of pharmacology and helped researchers rapidly identify promising chemical compounds for various diseases (5).”