ArticleBriefings in bioinformatics2023
A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs.
Article in Briefings in bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
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- Homocysteine-induced bone formation dysfunction reversed by icariin via the AKT/FOXO1 signaling pathway.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Identification of a metastasis-associated prognostic gene signature in osteosarcoma through integrated bioinformatics and functional analysis.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Luteolin ameliorates sympathetic stress-induced myocardial injury by inhibiting the VEGFA/VEGFR2 and PI3K/Akt/NF-kappaB signaling pathways.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- A multidimensional pan-cancer analysis of CD47 and its role in promoting malignant phenotype in pancreatic adenocarcinoma.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Integrated computational and experimental approaches for drug repurposing targeting key Mycobacterium tuberculosis proteins.Molecular diversity · 2026Article
- Pan-cancer multi-omics analysis identifies TOMM22 as an oncogenic driver and therapeutic target in LIHC via ferroptosis regulation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Article
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- Annotating genomes at increased scale and resolution.Nature reviews. Genetics · 2026Review
- Diosmetin, an active ingredient of Schizonepeta tenuifolia, alleviates acne by regulating inflammation and oxidative stress.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- PARP1 inhibitors discovery: innovative screening strategies incorporating machine learning and fragment replacement techniques.Molecular diversity · 2026Article
- Autophagy hub genes mediate photodynamic therapy tolerance in nasopharyngeal carcinoma through cytoprotective autophagy and survival signaling.Journal of molecular histology · 2026Article
- Data mining and experimental validation identify a core herb combination as a therapeutic agent for malignant lymphoma.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- TensoGraph: a tensor-transformer framework for global-local drug synergy prediction on heterogeneous graphs.Briefings in bioinformatics · 2026Article
- Mechanism identification of an herbal decoction for potential treatment of diffuse large B-cell lymphoma using network pharmacology and experimental validation.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Cosynllm: predicting drug combination synergy with LLM-generated descriptions.Journal of cheminformatics · 2026Article
- A review of deep learning approaches for drug synergy prediction in cancer.npj drug discovery · 2025Review
- Multi-omics analysis revealed heterogeneity of AARS1 and AARS2 in pan-cancer and identified AARS1 as a potential prognostic biomarker for urologic neoplasms.Apoptosis : an international journal on programmed cell death · 2025Article
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9 authors.
Funding
Abstract
Combination therapy has exhibited substantial potential compared to monotherapy. However, due to the explosive growth in the number of cancer drugs, the screening of synergistic drug combinations has become both expensive and time-consuming. Synergistic drug combinations refer to the concurrent use of two or more drugs to enhance treatment efficacy. Currently, numerous computational methods have been developed to predict the synergistic effects of anticancer drugs. However, there has been insufficient exploration of how to mine drug and cell line data at different granularity levels for predicting synergistic anticancer drug combinations. Therefore, this study proposes a granularity-level information fusion strategy based on the hypergraph transformer, named HypertranSynergy, to predict synergistic effects of anticancer drugs. HypertranSynergy introduces synergistic connections between cancer cell lines and drug combinations using hypergraph. Then, the Coarse-grained Information Extraction (CIE) module merges the hypergraph with a transformer for node embeddings. In the CIE module, Contranorm is a normalization layer that mitigates over-smoothing, while Gaussian noise addresses local information gaps. Additionally, the Fine-grained Information Extraction (FIE) module assesses fine-grained information's impact on predictions by employing similarity-aware matrices from drug/cell line features. Both CIE and FIE modules are integrated into HypertranSynergy. In addition, HypertranSynergy achieved the AUC of 0.93${\pm }$0.01 and the AUPR of 0.69${\pm }$0.02 in 5-fold cross-validation of classification task, and the RMSE of 13.77${\pm }$0.07 and the PCC of 0.81${\pm }$0.02 in 5-fold cross-validation of regression task. These results are better than most of the state-of-the-art models.
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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.