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Drug Response Prediction

Drug response prediction is about using computer methods to guess how someone will react to certain medicines. It involves looking at various types of data, like genes, drug structures, and medical records, to predict how well a person will respond to a particular treatment. The aim is to create personalized treatment plans for patients, ensuring they get the best results with the fewest side effects. This approach not only helps doctors choose the right medicines for each patient but also speeds up the development of new drugs by predicting their effectiveness and safety. Techniques like machine learning and deep learning are commonly used to make these predictions based on different types of data, such as genetics and medical history.

Papers

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Showing 31–40 of 46 papers

TitleStatusHype
A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior—0
Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology imagesCode2
AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural NetworksCode0
Distributionally Robust Multi-Output Regression Ranking—0
Deep Denerative Models for Drug Design and Response—0
ASGARD: A Single-cell Guided pipeline to Aid Repurposing of DrugsCode1
A cross-study analysis of drug response prediction in cancer cell lines—0
DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction—0
Learning Curves for Drug Response Prediction in Cancer Cell LinesCode0
Variational Autoencoder for Anti-Cancer Drug Response Prediction—0
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