ArticleCell reports methods2023
A hybrid deep forest-based method for predicting synergistic drug combinations.
Article in Cell reports methods, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 42 citations in OpenAlex.
- A dual granular balanced deep forest model for effective drug combination prediction.iScience · 2026Article
- Cosynllm: predicting drug combination synergy with LLM-generated descriptions.Journal of cheminformatics · 2026Article
- Multi-Task Cascade Forest Framework for Predicting Acute Toxicity across Species.Research (Washington, D.C.) · 2026Article
- KGLGANSynergy: knowledge graph-based local and global attention network for drug synergy prediction.Journal of translational medicine · 2025Article
- Article
- Hallmarks of artificial intelligence contributions to precision oncology.Nature cancer · 2025Review
- A Bayesian active learning platform for scalable combination drug screens.Nature communications · 2025Article
- CDFA: Calibrated deep feature aggregation for screening synergistic drug combinations.Frontiers in pharmacology · 2025Article
- MicroHDF: predicting host phenotypes with metagenomic data using a deep forest-based framework.Briefings in bioinformatics · 2024Article
- Machine Learning Techniques for Predicting Drug-Related Side Effects: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2024Article
- HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction.Journal of chemical information and modeling · 2024Article
- Review
- Deep centroid: a general deep cascade classifier for biomedical omics data classification.Bioinformatics (Oxford, England) · 2024Article
- Artificial Intelligence Application for Anti-tumor Drug Synergy Prediction.Current medicinal chemistry · 2024Review
- A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs.Briefings in bioinformatics · 2023Article
- Multi-task learning for predicting synergistic drug combinations based on auto-encoding multi-relational graphs.iScience · 2023Article
- Review of Predicting Synergistic Drug Combinations.Life (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Combination therapy is a promising approach in treating multiple complex diseases. However, the large search space of available drug combinations exacerbates challenge for experimental screening. To predict synergistic drug combinations in different cancer cell lines, we propose an improved deep forest-based method, ForSyn, and design two forest types embedded in ForSyn. ForSyn handles imbalanced and high-dimensional data in medium-/small-scale datasets, which are inherent characteristics of drug combination datasets. Compared with 12 state-of-the-art methods, ForSyn ranks first on four metrics for eight datasets with different feature combinations. We conduct a systematic analysis to identify the most appropriate configuration parameters. We validate the predictive value of ForSyn with cell-based experiments on several previously unexplored drug combinations. Finally, a systematic analysis of feature importance is performed on the top contributing features extracted by ForSyn. The resulting key genes may play key roles on corresponding cancers.
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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.