Please use the most recent reference for citing SynergyFinder:
Ianevski, A., Giri, K. A., Aittokallio, T., 2022.
SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples.
Nucleic Acids Research. gkac382, https://doi.org/10.1093/nar/gkac382
Ianevski, A., Giri, K. A., Aittokallio, T., 2020.
SynergyFinder 2.0: visual analytics of multi-drug combination synergies.
Nucleic Acids Research. gkaa216, https://doi.org/10.1093/nar/gkaa216
Ianevski, A., He, L., Aittokallio, T. and Tang, J., 2017.
SynergyFinder: a web application for analyzing drug combination dose–response matrix data.
Bioinformatics, 33(15), pp.2413-2415.
SynergyFinder uses cNMF algorithm to detect and replace outlier measurments. In case of replacing outlier measurment or predicting the missing response please cite:
Ianevski, A., Giri, A.K., Gautam, P., Kononov, A., Potdar, S., Saarela, J., Wennerberg, K. and Aittokallio, T., 2019.
Prediction of drug combination effects with a minimal set of experiments.
Nature Machine Intelligence, 1(12), pp.568-577.
The expected drug combination responses were calculated based on ZIP reference model using SynergyFinder [SynergyFinder reference]. Deviations between observed and expected responses with positive and negative values denote synergy and antagonism respectively.
For estimation of outlier measurments cNMF algorithm [Outlier detection reference] implemented in SynergyFinder was utilized.