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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 21–30 of 46 papers

TitleStatusHype
Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary InformationCode0
CLDR: Contrastive Learning Drug Response Models from Natural Language SupervisionCode0
TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representationCode0
Influencing factors on false positive rates when classifying tumor cell line response to drug treatment—0
Zero-shot Learning of Drug Response Prediction for Preclinical Drug ScreeningCode0
Precision Anti-Cancer Drug Selection via Neural RankingCode0
Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals—0
Deep learning methods for drug response prediction in cancer: predominant and emerging trends—0
Hybrid quantum neural network for drug response prediction—0
Towards a Better Model with Dual Transformer for Drug Response PredictionCode0
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