Drug response prediction using graph representation learning and ...

In this study, we propose a method called LGRDRP (Learning Graph Representation for Drug Response Prediction) to predict cell line-drug responses.

The learning graph representation method learns network topology structure features, and the Laplacian feature selection method further selects ...

Drug response prediction using graph representation learning and ...

ArticlePDF Available. Drug response prediction using graph representation learning and Laplacian feature selection. December 2022; BMC Bioinformatics 23(S8). 23 ...

An interpretable graph representation learning model for accurate ...

Evaluating molecular representations in machine learning models for drug response prediction and interpretability. J. Integr. Bioinform. (2022), p. 19. View ...

Graph Transformer for Drug Response Prediction - IEEE Xplore

Background : Previous models have shown that learning drug features from their graph representation is more efficient than learning from ...

Drug response prediction using graph representation ... - EBSCO

Title. Drug response prediction using graph representation learning and Laplacian feature selection. Authors. Xie, Minzhu; Lei, Xiaowen; Zhong, Jianchen; ...

Hypergraph Representation Learning for Cancer Drug Response ...

Accurately predicting drug response is crucial for personalized cancer treatment. Current graph neural network methods mainly focus on ...

Deep Graph and Sequence Representation Learning for Drug ...

Finally, we concatenate all representations through several dense layers and end with a regression layer to predict the response value.

Improving drug response prediction via integrating gene ...

Improving drug response prediction via integrating gene relationships with deep learning ... representations of drugs are molecular graphs.

Graph convolutional networks for drug response prediction

Finally, the two representations were then concatenated and put through two FC layers to predict the response. where W ∈ RF ×C is the trainable ...

GPDRP: a multimodal framework for drug response prediction with ...

By employing drug molecular graphs as the representation of drugs and leveraging GNN with Graph Transformer for feature extraction, this ...

Improving drug response prediction based on two-space graph ...

We design a heterogeneous graph convolutional network model to gather features from the heterogeneous nodes. Moreover, we generate final feature representations ...

Deep Graph and Sequence Representation Learning for Drug ...

Compare with [12, 13], other models [9,10,11] used convolutional neural networks (CNN) technology to predict drug response by integrating some ...

Graph Transformer for drug response prediction - bioRxiv

However, these models showed drawbacks in extracting drug features from graph representation and incorporating redundancy information from multi ...

MMDRP: drug response prediction and biomarker discovery using ...

To improve the representation and extraction of information from drug data in the context of DRP, we used a graph neural network (GNN) known as the AttentiveFP ...

Graph Convolutional Network for Drug Response Prediction Using ...

Reference [24] proposed a deep autoencoder model for representation learning of cancer cells from input data consisting of gene expression, CNV, ...

Deep learning methods for drug response prediction in cancer

A DRP model can be represented by r = f(d, c), where f is the analytical model designed to predict the response r of cancer c to the treatment by drug d. The ...

DRExplainer: Quantifiable Interpretability in Drug Response ... - arXiv

constructed a graph neural network with contrastive learning to enhance the generalization ability of drug response prediction [18] , Zhu et al.

(PDF) Graph convolutional networks for drug response prediction

representation. Finally, the 256-dimension vector, the combination of. drug's feature and cell line's feature is put through two. fully ...

[PDF] Graph convolutional networks for drug response prediction

Representing drugs as graphs can improve the performance of drug response prediction, and through saliency maps of the resulting GraphDRP models, ...