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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 1120 of 46 papers

TitleStatusHype
Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response PredictionCode0
Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data0
Controllable Edge-Type-Specific Interpretation in Multi-Relational Graph Neural Networks for Drug Response PredictionCode0
DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional NetworkCode0
Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses0
Regressor-free Molecule Generation to Support Drug Response Prediction0
Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence)Code0
WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancerCode0
Towards generalization of drug response prediction to single cells and patients utilizing importance-aware multi-source domain transfer learningCode0
Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests0
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