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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
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
A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior—0
Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data—0
Deep learning methods for drug response prediction in cancer: predominant and emerging trends—0
CellVerse: Do Large Language Models Really Understand Cell Biology?—0
Deep Denerative Models for Drug Design and Response—0
A cross-study analysis of drug response prediction in cancer cell lines—0
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