  The biological processes involved in a drug's mechanisms of action are
oftentimes dynamic, complex and difficult to discern. Time-course gene
expression data is a rich source of information that can be used to unravel
these complex processes, identify biomarkers of drug sensitivity and predict
the response to a drug. However, the majority of previous work has not fully
utilized this temporal dimension. In these studies, the gene expression data is
either considered at one time-point (before the administration of the drug) or
two timepoints (before and after the administration of the drug). This is
clearly inadequate in modeling dynamic gene-drug interactions, especially for
applications such as long-term drug therapy.
  In this work, we present a novel REcursive Prediction (REP) framework for
drug response prediction by taking advantage of time-course gene expression
data. Our goal is to predict drug response values at every stage of a long-term
treatment, given the expression levels of genes collected in the previous
time-points. To this end, REP employs a built-in recursive structure that
exploits the intrinsic time-course nature of the data and integrates past
values of drug responses for subsequent predictions. It also incorporates
tensor completion that can not only alleviate the impact of noise and missing
data, but also predict unseen gene expression levels (GELs). These advantages
enable REP to estimate drug response at any stage of a given treatment from
some GELs measured in the beginning of the treatment. Extensive experiments on
a dataset corresponding to 53 multiple sclerosis patients treated with
interferon are included to showcase the effectiveness of REP.
