Background: Determining a patient's response to a specific therapy is a vital step in
developing personalized cancer treatment. Personalized treatment relies on two key …

Extensive in vitro cancer drug screening datasets have enabled scientists to identify
biomarkers and develop machine learning models for predicting drug sensitivity. While most …

Predictive modeling holds great promise for improving personalized cancer treatment and
efficiency of drug development. In recent years, deep learning (DL) has been extensively …

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 …

In-depth modeling of the complex interplay among multiple omics data measured from
cancer cell lines or patient tumors is providing new opportunities toward identification of …

BACKGROUND: Clinical trials are controlled patient studies aiming to objectively assess the
effectiveness of treatment interventions. However, the average effectiveness observed at the …

Background: Few omics data-based prediction models have made a clinical impact due to
lack of access to real-world, clinically relevant datasets for method development and …

Drug response prediction arises from both basic and clinical research of personalized
therapy, as well as drug discovery for cancers. With gene expression profiles and other …

Translational models that utilize omics data generated in in vitro studies to predict the drug
efficacy of anti-cancer compounds in patients are highly distinct, which complicates the …

The prediction of anticancer drug response is crucial for achieving a more effective and
precise treatment of patients. Models based on the analysis of large cell line collections …