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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 1–10 of 46 papers

TitleStatusHype
Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology imagesCode2
scDrugMap: Benchmarking Large Foundation Models for Drug Response PredictionCode1
Integrating Random Forests and Generalized Linear Models for Improved Accuracy and InterpretabilityCode1
ASGARD: A Single-cell Guided pipeline to Aid Repurposing of DrugsCode1
Optimal normalization in quantum-classical hybrid models for anti-cancer drug response prediction—0
CellVerse: Do Large Language Models Really Understand Cell Biology?—0
Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction—0
GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction—0
Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysisCode0
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models—0
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