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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 1–25 of 46 papers

TitleStatusHype
Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology imagesCode2
scDrugMap: Benchmarking Large Foundation Models for Drug Response PredictionCode1
Integrating Random Forests and Generalized Linear Models for Improved Accuracy and InterpretabilityCode1
ASGARD: A Single-cell Guided pipeline to Aid Repurposing of DrugsCode1
Optimal normalization in quantum-classical hybrid models for anti-cancer drug response prediction—0
CellVerse: Do Large Language Models Really Understand Cell Biology?—0
Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction—0
GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction—0
Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysisCode0
GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models—0
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 Data—0
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 Responses—0
Regressor-free Molecule Generation to Support Drug Response Prediction—0
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 Forests—0
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
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