DrDimont - Drug Response Prediction from Differential Multi-Omics Networks
While it has been well established that drugs affect and
help patients differently, personalized drug response
predictions remain challenging. Solutions based on single omics
measurements have been proposed, and networks provide means to
incorporate molecular interactions into reasoning. However, how
to integrate the wealth of information contained in multiple
omics layers still poses a complex problem. We present a novel
network analysis pipeline, DrDimont, Drug response prediction
from Differential analysis of multi-omics networks. It allows
for comparative conclusions between two conditions and
translates them into differential drug response predictions.
DrDimont focuses on molecular interactions. It establishes
condition-specific networks from correlation within an omics
layer that are then reduced and combined into heterogeneous,
multi-omics molecular networks. A novel semi-local, path-based
integration step ensures integrative conclusions. Differential
predictions are derived from comparing the condition-specific
integrated networks. DrDimont's predictions are explainable,
i.e., molecular differences that are the source of high
differential drug scores can be retrieved. Our proposed
pipeline leverages multi-omics data for differential
predictions, e.g. on drug response, and includes prior
information on interactions. The case study presented in the
vignette uses data published by Krug (2020)
<doi:10.1016/j.cell.2020.10.036>. The package license applies
only to the software and explicitly not to the included data.
