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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 110 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
DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional NetworkCode0
AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural NetworksCode0
A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response PredictionCode0
Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysisCode0
CLDR: Contrastive Learning Drug Response Models from Natural Language SupervisionCode0
Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response PredictionCode0
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