Variational Autoencoder for Anti-Cancer Drug Response Prediction

Published in

arXiv.org(2020)

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TL;DR

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.

Abstract

Cancer has long been a main cause of human death, and the discovery of new drugs and the customization of cancer therapy have puzzled people for a long time. In order to facilitate the discovery of new anti-cancer drugs and the customization of treatment strategy, we seek to predict the response of different anti-cancer drugs with variational autoencoders (VAE) and multi-layer perceptron (MLP).Our model takes as input gene expression data of cancer cell lines and anti-cancer drug molecular data, and encode these data with {\sc {GeneVae}} model, which is an ordinary VAE, and rectified junction tree variational autoencoder ({\sc JtVae}) (\cite{jin2018junction}) model, respectively. Encoded features are processes by a Multi-layer Perceptron (MLP) model to produce a final prediction. We reach an average coefficient of determination ($R^{2} = 0.83$) in predicting drug response on breast cancer cell lines and an average $R^{2} > 0.84$ on pan-cancer cell lines. Additionally, we show that our model can generate unseen effective drug compounds for specific cancer cell lines.

Authors

Hongyuan Dong

1 papers

Jiaqing Xie

2 papers

Zhi Jing

1 papers

References44 items

DeepCDR: a hybrid graph convolutional network for predicting cancer drug response

Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models

Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders

Dr.VAE: improving drug response prediction via modeling of drug perturbation effects

Variational Autoencoder for Anti-Cancer Drug Response Prediction

Published in

arXiv.org(2020)

External Links:

Generate Graph

TL;DR

Abstract

Authors

Hongyuan Dong

1 papers

Jiaqing Xie

2 papers

Zhi Jing

1 papers

References44 items

DeepCDR: a hybrid graph convolutional network for predicting cancer drug response

Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models

CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models

Towards Explainable Anticancer Compound Sensitivity Prediction via Multimodal Attention-based Convolutional Encoders

Dr.VAE: improving drug response prediction via modeling of drug perturbation effects

PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks

De novo generation of hit-like molecules from gene expression signatures using artificial intelligence

Feature selection of gene expression data for Cancer classification using double RBF-kernels

Compound‐protein interaction prediction with end‐to‐end learning of neural networks for graphs and sequences

Cancer Drug Response Profile scan (CDRscan): A Deep Learning Model That Predicts Drug Effectiveness from Cancer Genomic Signature

Constrained Graph Variational Autoencoders for Molecule Design

Predicting drug response of tumors from integrated genomic profiles by deep neural networks

scVAE: Variational auto-encoders for single-cell gene expression data

Using Supervised Learning Methods for Gene Selection in RNA-Seq Case-Control Studies

Learning Deep Generative Models of Graphs

Junction Tree Variational Autoencoder for Molecular Graph Generation

GraphVAE: Towards Generation of Small Graphs Using Variational Autoencoders

Dr.VAE: Drug Response Variational Autoencoder

Neural Message Passing for Quantum Chemistry

Grammar Variational Autoencoder

Variational Graph Auto-Encoders

Recurrent Neural Network Grammars

Variational Inference: A Review for Statisticians

Convolutional Networks on Graphs for Learning Molecular Fingerprints

Deep clustering: Discriminative embeddings for segmentation and separation

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Dynamic-susceptibility contrast agent MRI measures of relative cerebral blood volume predict response to bevacizumab in recurrent high-grade glioma.

Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

Auto-Encoding Variational Bayes

Urine osmolality estimated using urine urea nitrogen, sodium and creatinine can effectively predict response to tolvaptan in decompensated heart failure patients.

Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells

The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

Biomarkers to predict response to sunitinib therapy and prognosis in metastatic renal cell cancer

Unsupervised Clustering Analysis of Gene Expression

A census of human cancer genes

2020) implement uniform graph convolutional neural networks (UGCNs) to extract

2020) also use gene mutation data and drug molecular data and apply CNNs

2018) use a CNN to encode gene mutation and drug molecular data, and Oskooei et al.Oskooei et al. (2018) implement attention-based neural networks to produce explainable encoded features

2018) use CNN-based methods on gene mutation profiles and drug molecular data

2018) propose a JTVAE that decomposes molecules into valid substructures

2018) to encode drug molecular data, learning attention weights by multihead-attention

2018) We also wish to explore incorporating attention mechanism-based models to improve performance.Manica et al

build graph CNNs on circular fingerprints of molecules

Visualizing Data using t-SNE

Field of Study

Computer ScienceMathematics

Journal Information

Name

ArXiv

Volume

abs/2005.00687

Venue Information

Name

arXiv.org

Type

URL

https://arxiv.org

Alternate Names

ArXiv