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Drug Response Prediction

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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.

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Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis

Towards generalization of drug response prediction to single cells and patients utilizing importance-aware multi-source domain transfer learning

Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests

Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information

CLDR: Contrastive Learning Drug Response Models from Natural Language Supervision

TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representation

Drug Response Prediction

논문

Optimal normalization in quantum-classical hybrid models for anti-cancer drug response prediction

CellVerse: Do Large Language Models Really Understand Cell Biology?

scDrugMap: Benchmarking Large Foundation Models for Drug Response Prediction

Integrating Single-Cell Foundation Models with Graph Neural Networks for Drug Response Prediction

GraphPINE: Graph Importance Propagation for Interpretable Drug Response Prediction

Benchmarking community drug response prediction models: datasets, models, tools, and metrics for cross-dataset generalization analysis

GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models

Foundation-Model-Boosted Multimodal Learning for fMRI-based Neuropathic Pain Drug Response Prediction

Assessing Reusability of Deep Learning-Based Monotherapy Drug Response Prediction Models Trained with Omics Data

Controllable Edge-Type-Specific Interpretation in Multi-Relational Graph Neural Networks for Drug Response Prediction

DRExplainer: Quantifiable Interpretability in Drug Response Prediction with Directed Graph Convolutional Network

Variational and Explanatory Neural Networks for Encoding Cancer Profiles and Predicting Drug Responses

Regressor-free Molecule Generation to Support Drug Response Prediction

WISER: Weak supervISion and supErvised Representation learning to improve drug response prediction in cancer

Towards generalization of drug response prediction to single cells and patients utilizing importance-aware multi-source domain transfer learning

Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests

Personalised Drug Identifier for Cancer Treatment with Transformers using Auxiliary Information

CLDR: Contrastive Learning Drug Response Models from Natural Language Supervision

TransCDR: a deep learning model for enhancing the generalizability of cancer drug response prediction through transfer learning and multimodal data fusion for drug representation

Influencing factors on false positive rates when classifying tumor cell line response to drug treatment

Zero-shot Learning of Drug Response Prediction for Preclinical Drug Screening

Integrating Random Forests and Generalized Linear Models for Improved Accuracy and Interpretability

Precision Anti-Cancer Drug Selection via Neural Ranking

Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals

Deep learning methods for drug response prediction in cancer: predominant and emerging trends

Hybrid quantum neural network for drug response prediction

Towards a Better Model with Dual Transformer for Drug Response Prediction

Prediction of drug effectiveness in rheumatoid arthritis patients based on machine learning algorithms

A Fair Experimental Comparison of Neural Network Architectures for Latent Representations of Multi-Omics for Drug Response Prediction

A Deep Bayesian Bandits Approach for Anticancer Therapy: Exploration via Functional Prior

Data augmentation and multimodal learning for predicting drug response in patient-derived xenografts from gene expressions and histology images

AGMI: Attention-Guided Multi-omics Integration for Drug Response Prediction with Graph Neural Networks

Distributionally Robust Multi-Output Regression Ranking

ASGARD: A Single-cell Guided pipeline to Aid Repurposing of Drugs

Deep Denerative Models for Drug Design and Response

A cross-study analysis of drug response prediction in cancer cell lines