# Precision oncology: Computational methods for multi-omics data integration to improve drug response prediction

## TL;DR

This paper reviews computational methods for combining multi-omics data to better predict cancer drug responses, aiming to improve personalized cancer treatment.

## Contribution

The paper provides a comprehensive review of computational approaches for integrating multi-omics data to enhance drug response prediction in precision oncology.

## Key findings

- Multi-omics data integration improves drug response prediction by capturing complex tumor biology.
- Advanced deep learning and multimodal frameworks outperform traditional ML methods in DRP tasks.
- Evaluation metrics like F1 score and mean square error are commonly used to assess model performance.

## Abstract

Cancer heterogeneity presents a major obstacle to effective drug treatment, emphasizing the need for personalized approaches that can accurately predict drug responses. Advances in high-throughput technologies have driven precision medicine initiatives toward integrating multi-omics data, enabling a more comprehensive understanding of tumor biology. However, integration of diverse omics layers poses challenges for computational modeling, as many traditional machine learning (ML) and statistical methods are not designed to capture complex, high-dimensional and multimodal data. This review examines the studies that integrate multi-omics datasets, aiming to enhance drug response prediction (DRP). Specifically, it outlines the most used omics types and computational approaches – classical ML models, as well as advanced deep learning and multimodal integration frameworks for improving DRP, detailing key methodologies and evaluation metrics, such as area under the dose–response curve, F1 score and mean square error, which assess model performance. By summarizing the integrated omics data, computational methods and challenges encountered, this review provides an in-depth overview of the existing landscape of precision medicine and future directions for advancing drug-response prediction.