ArticleBioinformatics (Oxford, England)2019
Dr.VAE: improving drug response prediction via modeling of drug perturbation effects.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers, 1 of them a synthesis that pooled it.
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Who cites it
99 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- AI-Driven Multi-Omics Integration of Synthetic Colon Adenocarcinoma for Cluster-Guided PROTAC Candidate Design Targeting KRASG12D.International journal of molecular sciences · 2026Article
- Topology-Aware Deep Learning on Higher-Order Structures for Drug Response Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- PertAdapt: unlocking single-cell foundation models for genetic perturbation prediction via condition-sensitive adaptation.Bioinformatics (Oxford, England) · 2026Article
- Review
- Gene regulatory networks: from correlative models to causal explanations.Nature reviews. Genetics · 2026Review
- Multimodal interpretable deep learning for transcriptome-informed precision oncology and drug mechanism analysis.NPJ digital medicine · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- CDCR-Rank: a computational model for predicting drug combination dose response using ranking-based optimization.Bioinformatics advances · 2026Article
- Machine learning-based prediction of drug response in ischemia reperfusion animal model.Scientific reports · 2025Article
- Decoding cell fate: integrated experimental and computational analysis at the single-cell level.Bioinformatics (Oxford, England) · 2025Review
- Fast-adapting graph neural network with prior knowledge for drug response prediction across preclinical and clinical data.Journal of pharmaceutical analysis · 2025Article
- Artificial Intelligence and Multi-Omics in Pharmacogenomics: A New Era of Precision Medicine.Mayo Clinic proceedings. Digital health · 2025Review
- Beyond digital twins: the role of foundation models in enhancing the interpretability of multiomics modalities in precision medicine.FEBS open bio · 2025Review
- Multi-omics based and AI-driven drug repositioning for epigenetic therapy in female malignancies.Journal of translational medicine · 2025Review
- Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.Interdisciplinary sciences, computational life sciences · 2025Article
- Role of Generative Artificial Intelligence in Personalized Medicine: A Systematic Review.Cureus · 2025Review
- Causal models and prediction in cell line perturbation experiments.BMC bioinformatics · 2025Article
- BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.PLoS computational biology · 2025Article
- Pan-Cancer Drug Sensitivity Prediction from Gene Expression using Deep Learning.bioRxiv : the preprint server for biology · 2024Article
39 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
motivationIndividualized drug response prediction is a fundamental part of personalized medicine for cancer. Great effort has been made to discover biomarkers or to develop machine learning methods for accurate drug response prediction in cancers. Incorporating prior knowledge of biological systems into these methods is a promising avenue to improve prediction performance. High-throughput cell line assays of drug-induced transcriptomic perturbation effects are a prior knowledge that has not been fully incorporated into a drug response prediction model yet.
resultsWe introduce a unified probabilistic approach, Drug Response Variational Autoencoder (Dr.VAE), that simultaneously models both drug response in terms of viability and transcriptomic perturbations. Dr.VAE is a deep generative model based on variational autoencoders. Our experimental results showed Dr.VAE to do as well or outperform standard classification methods for 23 out of 26 tested Food and Drug Administration-approved drugs. In a series of ablation experiments we showed that the observed improvement of Dr.VAE can be credited to the incorporation of drug-induced perturbation effects with joint modeling of treatment sensitivity. AVAILABILITY AND IMPLEMENTATION: Processed data and software implementation using PyTorch (Paszke et al., 2017) are available at: https://github.com/rampasek/DrVAE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.