Home Research Papers Datasets State of the Art Pricing

Discover, visualize, and connect AI research papers. Explore the latest trends and insights in artificial intelligence research.

Product

Home
Research Papers
About

Support

Contact
Terms of Service
Privacy Policy

medical-6

Drug Response Prediction

3260 papers • 126 benchmarks • 313 datasets

This task has no description! Would you like to contribute one?

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in drug-response-prediction-6

Trend

Dataset

Best Model

Actions

GDSC

Libraries

Use these libraries to find drug-response-prediction-6 models and implementations

Datasets

GDSC

Subtasks

No subtasks available.

Most implemented papers

Learning curves for drug response prediction in cancer cell lines

Austin R. Clyde, T. Brettin, A. Partin, H. Yoo, Rick L. Stevens, Yvonne A. Evrard, Yitan Zhu, Fangfang Xia, Songhao Jiang, Maulik Shukla, Michael Fonstein, J. Doroshow•Tue Nov 24 2020

Empirical learning curves are utilized for evaluating and comparing the data scaling properties of two neural networks and two gradient boosting decision tree models trained on four cell line drug screening datasets, demonstrating the benefit of using learning curves to evaluate prediction models and providing a broader perspective on the overall data scaling characteristics.

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

22 0

Paper Graph

Variational Autoencoder for Anti-Cancer Drug Response Prediction

Hongyuan Dong, Jiaqing Xie, Zhi Jing, Dexin Ren•Fri Aug 21 2020

This work seeks to predict the response of different anti-cancer drugs with variational autoencoders (VAE) and multi-layer perceptron (MLP) and shows that the model can generate unseen effective drug compounds for specific cancer cell lines.

15 0

Paper Graph

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

Sara E Kochanny, A. Pearson, T. Brettin, A. Partin, J. Dolezal, Yvonne A. Evrard, Yitan Zhu, Maulik Shukla, J. Doroshow, R. Stevens•Sun Apr 24 2022

The results show that data augmentation and integration of histology images with GE can improve prediction performance of drug response in PDXs and the multimodal learning based on the MCC metric, MM-Net outperforms all the baselines.

10 0

Paper Graph

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

Ruiwei Feng, Yufeng Xie, Minshan Lai, D. Chen, Ji Cao, Jian Wu•Wed Dec 08 2021

A novel Attention-Guided Multi-omics Integration (AGMI) approach for DRP, which first constructs a Multiedge Graph for each cell line, and then aggregates multi-omics features to predict drug response using a novel structure, called Graph edge-aware Network (GeNet).

13 0

Paper Graph

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

Shengjia Chen, Nikunj Gupta, Woodward B. Galbraith, Valay Shah, Jacopo Cirrone•Thu Oct 13 2022

This study introduced a Drug Response Prediction (DRP) framework that provides a complete pipeline to predict disease activity scores and identify the group that does not respond well to anti-TNF treatments, thus showing promise in supporting clinical decisions based on EHR information.

2 0

Paper Graph

A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction

Tony Hauptmann, Stefan Kramer•Tue Aug 30 2022

A comparison framework that trains and optimizes multi-omics integration methods under equal conditions and devised a novel method, Omics Stacking, that combines the advantages of intermediate and late integration.

5 0

Paper Graph

TransEDRP: Dual Transformer model with Edge Emdedded for Drug Respond Prediction

Kun Li, Jia Wu, Bo Du, Sergey V. Petoukhov, Huiting Xu, Zheman Xiao, Wenbin Hu•Fri Dec 31 2021

The decoupled dual transformer structure with edge embedded for drug respond prediction (TransEDRP), which is used for the representation of cell line genomics and drug respectively, is proposed, which is better than the current mainstream approach in all evaluation indicators.

1 0

Paper Graph

Precision Anti-Cancer Drug Selection via Neural Ranking

V. Dey, Xia Ning•Thu Jun 29 2023

This work proposed two neural listwise ranking methods that learn latent representations of drugs and cell lines, and then use those representations to score drugs in each cell line via a learnable scoring function, on top of the existing method ListNet.

0 0

Paper Graph

Adding a benchmark result helps the community track progress.

Drug Response Prediction | State-of-the-Art