Drug-induced gene expression profiles are rich information sources that can help
to measure the effect of a drug on the transcriptional state of cells. However, the
available experimental data only covers a limited set of conditions such as treatment
time, dosages, and cell lines. This poses a challenge for neural network models to
learn embeddings that can be generalized to new experimental conditions. In this
project, we focus on the cell line as the confounder variable and train an Adversarial
Neural Network to extract transcriptional effects that are conserved across multiple
cell lines, and can thus be more confidently generalized to the biological setting of
interest. Additionally, we investigate several methods to test whether our approach
can simultaneously learn biologically valid embeddings and deconfound the effect of
cell lines on the data distribution
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/676473 |
| Date | 24 April 2022 |
| Creators | Alsulami, Reem A. |
| Contributors | Gao, Xin, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Hoehndorf, Robert, Moshkov, Mikhail, Napolitano, Francesco |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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