ArticleNature communications2021
Deep generative neural network for accurate drug response imputation.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed, 87 citations in OpenAlex.
- Expression quantitative trait methylation across multiple cancer types with functional and therapeutic characterization using Onco-eQTM.NAR genomics and bioinformatics · 2026Article
- Enhancing cross-context generalization in drug perturbation prediction with a multimodal conditional diffusion framework.Bioinformatics (Oxford, England) · 2026Article
- The impact of deep learning and omics data in transforming precision therapy for brain cancer.Frontiers in bioinformatics · 2026Review
- Identification of Clinically Relevant Genes Using Machine Learning Techniques.Methods in molecular biology (Clifton, N.J.) · 2026Article
- The Evolution of Machine Learning in Medicinal Chemistry: A Comprehensive Bibliometric Analysis.Current neuropharmacology · 2026Review
- Functional, Pharmacogenomic, and Immune Landscapes of Long Non-Coding RNAs in Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Computational limitations and future needs to unravel the full potential of 2'-O-methylation and C/D box snoRNAs.RNA biology · 2025Review
- Fast-adapting graph neural network with prior knowledge for drug response prediction across preclinical and clinical data.Journal of pharmaceutical analysis · 2025Article
- A pan-cancer, pan-treatment model for predicting drug responses from patient-derived xenografts.NAR genomics and bioinformatics · 2025Article
- PharmaFormer predicts clinical drug responses through transfer learning guided by patient derived organoid.NPJ precision oncology · 2025Article
- Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.Scientific reports · 2025Article
- Drug Sensitivity Prediction Based on Multi-stage Multi-modal Drug Representation Learning.Interdisciplinary sciences, computational life sciences · 2025Article
- A disentangled generative model for improved drug response prediction in patients via sample synthesis.Journal of pharmaceutical analysis · 2025Article
- Large-scale information retrieval and correction of noisy pharmacogenomic datasets through residual thresholded deep matrix factorization.Briefings in bioinformatics · 2025Article
- iGTP: learning interpretable cellular embedding for inferring biological mechanisms underlying single-cell transcriptomics.Briefings in bioinformatics · 2025Article
- Applications of Artificial Intelligence in Drug Repurposing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- A multi-task domain-adapted model to predict chemotherapy response from mutations in recurrently altered cancer genes.iScience · 2025Article
- Data imbalance in drug response prediction: multi-objective optimization approach in deep learning setting.Briefings in bioinformatics · 2025Article
- Ancestral Differences in Anticancer Treatment Efficacy and Their Underlying Genomic and Molecular Alterations.Cancer discovery · 2025Article
- Variational Approaches for Drug-Disease-Gene Links in Periodontal Inflammation.International dental journal · 2025Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
Abstract
Drug response differs substantially in cancer patients due to inter- and intra-tumor heterogeneity. Particularly, transcriptome context, especially tumor microenvironment, has been shown playing a significant role in shaping the actual treatment outcome. In this study, we develop a deep variational autoencoder (VAE) model to compress thousands of genes into latent vectors in a low-dimensional space. We then demonstrate that these encoded vectors could accurately impute drug response, outperform standard signature-gene based approaches, and appropriately control the overfitting problem. We apply rigorous quality assessment and validation, including assessing the impact of cell line lineage, cross-validation, cross-panel evaluation, and application in independent clinical data sets, to warrant the accuracy of the imputed drug response in both cell lines and cancer samples. Specifically, the expression-regulated component (EReX) of the observed drug response achieves high correlation across panels. Using the well-trained models, we impute drug response of The Cancer Genome Atlas data and investigate the features and signatures associated with the imputed drug response, including cell line origins, somatic mutations and tumor mutation burdens, tumor microenvironment, and confounding factors. In summary, our deep learning method and the results are useful for the study of signatures and markers of drug response.
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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.