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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 1–25 of 46 papers

TitleStatusHype
Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology imagesCode2
ASGARD: A Single-cell Guided pipeline to Aid Repurposing of DrugsCode1
scDrugMap: Benchmarking Large Foundation Models for Drug Response PredictionCode1
Integrating Random Forests and Generalized Linear Models for Improved Accuracy and InterpretabilityCode1
Deep Denerative Models for Drug Design and Response—0
Regressor-free Molecule Generation to Support Drug Response Prediction—0
REP: Predicting the Time-Course of Drug Sensitivity—0
Selective Inference for Sparse High-Order Interaction Models—0
Deep learning methods for drug response prediction in cancer: predominant and emerging trends—0
Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals—0
Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses—0
Variational Autoencoder for Anti-Cancer Drug Response Prediction—0
Distributionally Robust Multi-Output Regression Ranking—0
DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction—0
Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data—0
Drug response prediction by ensemble learning and drug-induced gene expression signatures—0
Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization—0
Dr.VAE: Drug Response Variational Autoencoder—0
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
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