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The biological processes involved in a drug's mechanisms of action areoftentimes dynamic, complex and difficult to discern. Time-course geneexpression data is a rich source of information that can be used to unravelthese complex processes, identify biomarkers of drug sensitivity and predictthe response to a drug. However, the majority of previous work has not fullyutilized this temporal dimension. In these studies, the gene expression data iseither considered at one time-point (before the administration of the drug) ortwo timepoints (before and after the administration of the drug). This isclearly inadequate in modeling dynamic gene-drug interactions, especially forapplications such as long-term drug therapy.  In this work, we present a novel REcursive Prediction (REP) framework fordrug response prediction by taking advantage of time-course gene expressiondata. Our goal is to predict drug response values at every stage of a long-termtreatment, given the expression levels of genes collected in the previoustime-points. To this end, REP employs a built-in recursive structure thatexploits the intrinsic time-course nature of the data and integrates pastvalues of drug responses for subsequent predictions. It also incorporatestensor completion that can not only alleviate the impact of noise and missingdata, but also predict unseen gene expression levels (GELs). These advantagesenable REP to estimate drug response at any stage of a given treatment fromsome GELs measured in the beginning of the treatment. Extensive experiments ona dataset corresponding to 53 multiple sclerosis patients treated withinterferon are included to showcase the effectiveness of REP.

REP: Predicting the Time-Course of Drug Sensitivity