Background and objectives The prediction of multiple drug efficacies using machine
learning prediction techniques based on clinical and molecular attributes of tumors is a new …

Background and objectives The computational prediction of drug responses based on the
analysis of multiple clinical features of the tumor will be a novel strategy for accomplishing …

Targeted therapies for inhibiting the growth of cancer cells or inducing apoptosis are
urgently needed for effective rhabdomyosarcoma (RMS) treatment. However, identifying …

Drug response prediction is important to establish personalized medicine for cancer therapy.
Model construction for predicting drug response (ie, cell viability half-maximal inhibitory …

Radiation and hormone level targeted drug therapy are one of the most widely adopted
treatment options for different types of cancer. But, due to genetic variations, cancer patients …

Predicting oncologic outcome is challenging due to the diversity of cancer histologies and
the complex network of underlying biological factors. In this study, we determine whether …

Objectives Accumulating evidence argues for a more widespread use of therapeutic drug
monitoring (TDM) to support individualized medicine, especially for therapies where toxicity …

Background A challenge in precision medicine is the transformation of genomic data into
knowledge that can be used to stratify patients into treatment groups based on predicted …

Motivation One of the most difficult challenges in precision medicine is determining the best
treatment strategy for each patient based on personal information. Since drug response …

Background: Cancer cell lines are frequently used in research as in-vitro tumor models.
Genomic data and large-scale drug screening have accelerated the right drug selection for …