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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 41–46 of 46 papers

TitleStatusHype
Distributionally Robust Multi-Output Regression Ranking—0
DIVERSE: Bayesian Data IntegratiVE learning for precise drug ResponSE prediction—0
Variational Autoencoder for Anti-Cancer Drug Response Prediction—0
Drug response prediction by ensemble learning and drug-induced gene expression signatures—0
Drug response prediction by inferring pathway-response associations with Kernelized Bayesian Matrix Factorization—0
Dr.VAE: Drug Response Variational Autoencoder—0
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