ArticleBMC medical genomics2019
Predicting drug response of tumors from integrated genomic profiles by deep neural networks.
Article in BMC medical genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 114 papers.
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Who cites it
114 citing papers in PubMed, 228 citations in OpenAlex.
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- Deep Learning for Biomarker Discovery in Cancer Genomes.bioRxiv : the preprint server for biology · 2025Article
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- A drug response prediction method for single-cell tumors combining attention networks and transfer learning.Frontiers in medicine · 2025Article
- Editorial: Multi-scale systems: ecological approaches to investigate the role of the microbiota in different niches.Frontiers in molecular biosciences · 2025Article
- Artificial intelligence in oncology drug development and management: a precision medicine perspective.Frontiers in oncology · 2025Review
- Two-Step Transfer Learning Improves Deep Learning-Based Drug Response Prediction in Small Datasets: A Case Study of Glioblastoma.Bioinformatics and biology insights · 2025Article
- AI reshaping life sciences: intelligent transformation, application challenges, and future convergence in neuroscience, biology, and medicine.Frontiers in digital health · 2025Review
54 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundThe study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent study screened for the response of a thousand human cancer cell lines to a wide collection of anti-cancer drugs and illuminated the link between cellular genotypes and vulnerability. However, due to essential differences between cell lines and tumors, to date the translation into predicting drug response in tumors remains challenging. Recently, advances in deep learning have revolutionized bioinformatics and introduced new techniques to the integration of genomic data. Its application on pharmacogenomics may fill the gap between genomics and drug response and improve the prediction of drug response in tumors.
resultsWe proposed a deep learning model to predict drug response (DeepDR) based on mutation and expression profiles of a cancer cell or a tumor. The model contains three deep neural networks (DNNs), i) a mutation encoder pre-trained using a large pan-cancer dataset (The Cancer Genome Atlas; TCGA) to abstract core representations of high-dimension mutation data, ii) a pre-trained expression encoder, and iii) a drug response predictor network integrating the first two subnetworks. Given a pair of mutation and expression profiles, the model predicts IC
conclusionsHere we present, as far as we know, the first DNN model to translate pharmacogenomics features identified from in vitro drug screening to predict the response of tumors. The results covered both well-studied and novel mechanisms of drug resistance and drug targets. Our model and findings improve the prediction of drug response and the identification of novel therapeutic options.
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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.