Abstract
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. The method represents a modified version of the LOBICO method and yields easily interpretable models amenable to a Boolean logic-based interpretation. Since the proposed altered logical rules lead to an enormous acceleration of the running times of MERIDA compared to LOBICO, we cannot only consider larger input feature sets integrated from genetic and molecular omics data but also build more comprehensive models that mirror the complexity of cancer initiation and progression. Moreover, we enable the inclusion of a priori knowledge that can either stem from biomarker databases or can also be newly acquired knowledge gathered iteratively by previous runs of MERIDA. Our results show that this approach does not only lead to an improved predictive performance but also identifies a variety of putative sensitivity and resistance biomarkers. We also compare our approach to state-of-the-art machine learning methods and demonstrate the superior performance of our method. Hence, MERIDA has great potential to deepen our understanding of the molecular mechanisms causing drug sensitivity or resistance.
Availability and implementation
The corresponding code is available on github (https://github.com/unisb-bioinf/MERIDA.git)
Supplementary information
Supplementary data are available at Bioinformatics online.
