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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

Showing 2130 of 46 papers

TitleStatusHype
GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction0
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models0
Hybrid quantum neural network for drug response prediction0
Influencing factors on false positive rates when classifying tumor cell line response to drug treatment0
Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction0
A cross-study analysis of drug response prediction in cancer cell lines0
Optimal normalization in quantum-classical hybrid models for anti-cancer drug response prediction0
CellVerse: Do Large Language Models Really Understand Cell Biology?0
A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior0
Zero-shot Learning of Drug Response Prediction for Preclinical Drug ScreeningCode0
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