SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 21–30 of 46 papers

TitleStatusHype
Zero-shot Learning of Drug Response Prediction for Preclinical Drug ScreeningCode0
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests—0
Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response—0
GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction—0
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models—0
Hybrid quantum neural network for drug response prediction—0
Influencing factors on false positive rates when classifying tumor cell line response to drug treatment—0
Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction—0
Regressor-free Molecule Generation to Support Drug Response Prediction—0
A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior—0
Show:102550
← PrevPage 3 of 5Next →

No leaderboard results yet.