ArticleJournal of the American Medical Informatics Association : JAMIA2021
Anticancer drug synergy prediction in understudied tissues using transfer learning.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed.
- TensoGraph: a tensor-transformer framework for global-local drug synergy prediction on heterogeneous graphs.Briefings in bioinformatics · 2026Article
- PAIRWISE: Deep Learning-based Prediction of Effective Personalized Drug Combinations in Cancer.Research square · 2026Article
- A review of deep learning approaches for drug synergy prediction in cancer.npj drug discovery · 2025Review
- SynergyImage: image-based model for drug combinations synergy score prediction.BMC bioinformatics · 2025Article
- Few-shot drug synergy prediction via rapid cross-tier adaptation meta-optimization.Briefings in bioinformatics · 2025Article
- SynVerse: a modular framework for building and evaluating deep learning-based drug synergy prediction models.Briefings in bioinformatics · 2025Article
- Building a unified model for drug synergy analysis powered by large language models.Nature communications · 2025Article
- Out of distribution learning in bioinformatics: advancements and challenges.Briefings in bioinformatics · 2025Review
- Anticancer drug synergy prediction based on CatBoost.PeerJ. Computer science · 2025Article
- Self-Supervised Based Multi-View Graph Presentation Learning for Drug-Drug Interaction Prediction.Transactions on artificial intelligence · 2025Article
- SynDRep: a synergistic partner prediction tool based on knowledge graph for drug repurposing.Bioinformatics advances · 2025Article
- MMFSyn: A Multimodal Deep Learning Model for Predicting Anticancer Synergistic Drug Combination Effect.Biomolecules · 2024Article
- Transformers and large language models in healthcare: A review.Artificial intelligence in medicine · 2024Review
- Trust me if you can: a survey on reliability and interpretability of machine learning approaches for drug sensitivity prediction in cancer.Briefings in bioinformatics · 2024Review
- piscesCSM: prediction of anticancer synergistic drug combinations.Journal of cheminformatics · 2024Article
- PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction.Journal of cheminformatics · 2024Article
- CancerGPT for few shot drug pair synergy prediction using large pretrained language models.NPJ digital medicine · 2024Article
- Predicting anticancer synergistic drug combinations based on multi-task learning.BMC bioinformatics · 2023Article
- DeepTraSynergy: drug combinations using multimodal deep learning with transformers.Bioinformatics (Oxford, England) · 2023Article
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
objectiveDrug combination screening has advantages in identifying cancer treatment options with higher efficacy without degradation in terms of safety. A key challenge is that the accumulated number of observations in in-vitro drug responses varies greatly among different cancer types, where some tissues are more understudied than the others. Thus, we aim to develop a drug synergy prediction model for understudied tissues as a way of overcoming data scarcity problems. MATERIALS AND
methodsWe collected a comprehensive set of genetic, molecular, phenotypic features for cancer cell lines. We developed a drug synergy prediction model based on multitask deep neural networks to integrate multimodal input and multiple output. We also utilized transfer learning from data-rich tissues to data-poor tissues.
resultsWe showed improved accuracy in predicting synergy in both data-rich tissues and understudied tissues. In data-rich tissue, the prediction model accuracy was 0.9577 AUROC for binarized classification task and 174.3 mean squared error for regression task. We observed that an adequate transfer learning strategy significantly increases accuracy in the understudied tissues.
conclusionsOur synergy prediction model can be used to rank synergistic drug combinations in understudied tissues and thus help to prioritize future in-vitro experiments. Code is available at https://github.com/yejinjkim/synergy-transfer.
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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.