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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 31–40 of 46 papers

TitleStatusHype
Optimal normalization in quantum-classical hybrid models for anti-cancer drug response prediction—0
REP: Predicting the Time-Course of Drug Sensitivity—0
A cross-study analysis of drug response prediction in cancer cell lines—0
CellVerse: Do Large Language Models Really Understand Cell Biology?—0
Selective Inference for Sparse High-Order Interaction Models—0
Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses—0
Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data—0
Deep Denerative Models for Drug Design and Response—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
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