ArticlePloS one2024
DeepDRA: Drug repurposing using multi-omics data integration with autoencoders.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Artificial intelligence and extracellular vesicles in oncology: towards tumor diagnosis, prediction, and therapy.Drug delivery · 2026Review
- 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
- The MOGA multi-modal framework based on graph augmentation networks for drug response prediction.iScience · 2026Article
- Associations of Normalization and Regularization with Machine Learning Overfitting in Cross-dataset Classification of Deaths Using Transcriptomic and Clinical Data: A Secondary Analysis of Publicly Available Databases.Journal of clinical and translational pathology · 2026Article
- A novel explainable AI for revealing determinants of cancer drug response through integrative multi-omics analysis.Frontiers in oncology · 2026Article
- The Application of Omics Technologies in Type II Diabetes Mellitus Research.Current diabetes reviews · 2026Review
- Article
- A Novel Integrative Framework for Depression: Combining Network Pharmacology, Artificial Intelligence, and Multi-Omics with a Focus on the Microbiota-Gut-Brain Axis.Current issues in molecular biology · 2025Review
- Artificial intelligence in bioinformatics: a survey.Briefings in bioinformatics · 2025Review
- ProjectSVR: mapping single-cell RNA-seq data to reference atlases by supported vector regression.Briefings in bioinformatics · 2025Article
- Artificial Intelligence and Multi-Omics in Pharmacogenomics: A New Era of Precision Medicine.Mayo Clinic proceedings. Digital health · 2025Review
- Deep dictionary learning with reconstruction for texture recognition.Scientific reports · 2025Article
- A large language model for predicting neurotoxic peptides and neurotoxins.Protein science : a publication of the Protein Society · 2025Article
- DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.Journal of pharmaceutical analysis · 2025Article
- Multi-omics based and AI-driven drug repositioning for epigenetic therapy in female malignancies.Journal of translational medicine · 2025Review
- Accurate prediction of synergistic drug combination using a multi-source information fusion framework.BMC biology · 2025Article
- Advancing genome-based precision medicine: a review on machine learning applications for rare genetic disorders.Briefings in bioinformatics · 2025Review
- Joint fusion of sequences and structures of drugs and targets for identifying targets based on intra and inter cross-attention mechanisms.BMC biology · 2025Article
- Spatial correlation guided cross scale feature fusion for age and gender estimation.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer treatment has become one of the biggest challenges in the world today. Different treatments are used against cancer; drug-based treatments have shown better results. On the other hand, designing new drugs for cancer is costly and time-consuming. Some computational methods, such as machine learning and deep learning, have been suggested to solve these challenges using drug repurposing. Despite the promise of classical machine-learning methods in repurposing cancer drugs and predicting responses, deep-learning methods performed better. This study aims to develop a deep-learning model that predicts cancer drug response based on multi-omics data, drug descriptors, and drug fingerprints and facilitates the repurposing of drugs based on those responses. To reduce multi-omics data's dimensionality, we use autoencoders. As a multi-task learning model, autoencoders are connected to MLPs. We extensively tested our model using three primary datasets: GDSC, CTRP, and CCLE to determine its efficacy. In multiple experiments, our model consistently outperforms existing state-of-the-art methods. Compared to state-of-the-art models, our model achieves an impressive AUPRC of 0.99. Furthermore, in a cross-dataset evaluation, where the model is trained on GDSC and tested on CCLE, it surpasses the performance of three previous works, achieving an AUPRC of 0.72. In conclusion, we presented a deep learning model that outperforms the current state-of-the-art regarding generalization. Using this model, we could assess drug responses and explore drug repurposing, leading to the discovery of novel cancer drugs. Our study highlights the potential for advanced deep learning to advance cancer therapeutic precision.
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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.