Systems medicine approaches to cancer drug resistance and response, towards new clinical tools

I generated new gold-standard datasets to access the performance of the new weighted methods and implemented a training workflow for wNetNC to utilise high-performance cluster computing to reduce the computational time requirements and manage the high number of output files generated.<br><br>Benchmarking against existing network analysis methods (NetNC, NEST, and HC-PIN) showed that wNetNC performed well, often showing significantly better performance, especially when using low or medium noise level genelists.

NEST and HC-PIN achieved higher performance than wNetNC at high noise levels. <br>I applied NetNC and wNetNC to an RNA-seq dataset to investigate how network analysis can be applied to single sample data. Additionally, I interpreted the network models for this data to determine the driving mechanisms controlling resistance to radiotherapy in prostate cancer. I developed an analytical workflow to rank the network genes as suitable druggable targets and predicted two novel candidates for drug repurposing to reduce resistance to radiotherapy in prostate cancer.<br><br><br>