Drug interactions and the evolution of antibiotic resistance
By P. Yeh, Matthew J. Hegreness, A. P. Aiden, Roy Kishony. 2009
346 citations recorded by the source corpus.
Large-scale, systems biology approaches now allow us to systematically map synergistic and antagonistic interactions between drugs. Consequently, drug antagonism is emerging as a powerful tool to study biological function and relatedness between cellular components as well as to uncover mechanisms of drug action. Furthermore, theoretical models and new experiments suggest that antagonistic interactions between antibiotics can counteract the evolution of drug resistance.
Subfields: Computational Theory and Mathematics, Genetics, 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
- Marinka Zitnik
- Jure Leskovec
- Anna Goldenberg
- Rajarshi Guha
- Trey Ideker
- Andreas Bender
- Tamás Korcsmáros
- Richard P. I. Lewis
In Genetics
In Molecular Biology
- Marinka Zitnik
- Jure Leskovec
- Anna Goldenberg
- Michael M. Hoffman
- Francis Nguyen
- Trey Ideker
- Samson H. Fong
- John J. Lee
Citations in the corpus
- Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, background): “It is often defined through the concepts of synergy and antagonism [251].”
- Predicting Drug Response and Synergy Using a Deep Learning Model of Human Cancer Cells (2020, background): “The parallel pathway inhibition theory of drug synergy (Yeh et al., 2009) holds that two drugs will be synergistic if they inhibit separate pathways that regulate a common essential function (Figure 5A).”
- Synergy Maps: exploring compound combinations using network-based visualization (2015, background): “2) for all pairs combination data is also popular [3, 15, 16, 24, 25, 32]—nodes correspond to compounds, and edges to combinations, connecting their components.”