Article citationsMore>>

Virtanen, P., Gommers, R., Oliphant, T.E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S.J., Brett, M., Wilson, J., Millman, K.J., Mayorov, N., Nelson, A.R.J., Jones, E., Kern, R., Larson, E., Carey, C.J., Polat, I., Feng, Y., Moore, E.W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E.A., Harris, C.R., Archibald, A.M., Ribeiro, A.H., Pedregosa, F., van Mulbregt, P., SciPy1.0 Contributors. (2020) Author Correction: SciPy1.0: Fundamental Algorithms for Scientific Computing in Python. Nature Methods, 17, 261-272.
https://doi.org/10.1038/s41592-020-0772-5

has been cited by the following article:

• TITLE: Drug-Treatment Generation Combinatorial Algorithm Based on Machine Learning and Statistical Methodologies

  AUTHORS: Karen Gishyan

  KEYWORDS: Combinatorial Treatments, Health Informatics, Machine Learning

  JOURNAL NAME: Open Journal of Applied Sciences, Vol.13 No.4, April 28, 2023

  ABSTRACT: Finding out the desired drug combinations is a challenging task because of the number of different combinations that exist and the adversarial effects that may arise. In this work, we generate drug combinations over multiple stages using distance calculation metrics from supervised learning, clustering, and a statistical similarity calculation metric for deriving the optimal treatment sequences. The combination generation happens for each patient based on the characteristics (features) observed during each stage of treatment. Our approach considers not the drug-to-drug (one-to-one) effect, but rather the effect of group of drugs with another group of drugs. We evaluate the combinations using an FNN model and identify future improvement directions.