ArticleComputational and mathematical methods in medicine2022
DCE-DForest: A Deep Forest Model for the Prediction of Anticancer Drug Combination Effects.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 13 citations in OpenAlex.
- Advancing bioinformatics with language models: components, applications, and perspectives.Briefings in bioinformatics · 2026Review
- KGLGANSynergy: knowledge graph-based local and global attention network for drug synergy prediction.Journal of translational medicine · 2025Article
- Attention is all you need: utilizing attention in AI-enabled drug discovery.Briefings in bioinformatics · 2023Review
- Cancer Informatics 2023: Data Sharing and Federating Learning Point Towards New Collaborative Opportunities.Yearbook of medical informatics · 2023Article
- A hybrid deep forest-based method for predicting synergistic drug combinations.Cell reports methods · 2023Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
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Abstract
Drug combinations have recently been studied intensively due to their critical role in cancer treatment. Computational prediction of drug synergy has become a popular alternative strategy to experimental methods for anticancer drug synergy predictions. In this paper, a deep learning model called DCE-DForest is proposed to predict the synergistic effect of drug combinations. To sufficiently extract drug information, the paper leverages BERT (Bidirectional Encoder Representations from Transformers) to encode the drug and the deep forest to model the nonlinear relationship between the drugs and cell lines. The experimental results on the synergy datasets demonstrate that the proposed method consistently shows superior performance over the other machine learning models.
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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.