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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 26–46 of 46 papers

TitleStatusHype
Deep learning methods for drug response prediction in cancer: predominant and emerging trends—0
Hybrid quantum neural network for drug response prediction—0
Towards a Better Model with Dual Transformer for Drug Response PredictionCode0
Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithmsCode0
A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response PredictionCode0
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
Ensemble Transfer Learning for the Prediction of Anti-Cancer Drug Response—0
REP: Predicting the Time-Course of Drug Sensitivity—0
Drug response prediction by ensemble learning and drug-induced gene expression signatures—0
Selective Inference for Sparse High-Order Interaction Models—0
Dr.VAE: Drug Response Variational Autoencoder—0
Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization—0
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