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Tim Kehl

Motivation A major goal of personalized medicine in oncology is the optimization of treatment strategies given measurements of the genetic and molecular profiles of cancer cells. To further our knowledge on drug sensitivity, machine learning techniques are commonly applied to cancer cell line panels. Results We present a novel integer linear programming formulation, called MEthod for Rule Identification with multi-omics DAta (MERIDA), for predicting the drug sensitivity of cancer cells.
Which genes, gene sets or pathways are regulated by certain miRNAs? Which miRNAs regulate a particular target gene or target pathway in a certain physiological context? Answering such common research questions can be time consuming and labor intensive. Especially for researchers without computational experience, the integration of different data sources, selection of the right parameters and concise visualization can be demanding. A comprehensive analysis should be central to present adequate answers to complex biological questions.
MicroRNAs are regulators of gene expression. A wide-spread, yet not validated, assumption is that the targetome of miRNAs is non-randomly distributed across the transcriptome and that targets share functional pathways. We developed a computational and experimental strategy termed high-throughput miRNA interaction reporter assay (HiTmIR) to facilitate the validation of target pathways. First, targets and target pathways are predicted and prioritized by computational means to increase the specificity and positive predictive value.

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