ArticleBioinformatics (Oxford, England)2024
BioPred: an R package for biomarkers analysis in precision medicine.
Article in Bioinformatics (Oxford, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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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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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
3 citing papers in PubMed.
- Human biomarker navigator.iMeta · 2026Review
- A Structured Computational Roadmap for Lipidomics in R: Reproducible Workflows from Raw Data to Functional Insight.Metabolites · 2026Review
- Deep learning-based ranking method for subgroup and predictive biomarker identification in patients.Communications medicine · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
summaryThe R package BioPred offers a suite of tools for subgroup and biomarker analysis in precision medicine. Leveraging Extreme Gradient Boosting (XGBoost) along with propensity score weighting and A-learning methods, BioPred facilitates the optimization of individualized treatment rules to streamline subgroup identification. BioPred also enables the identification of predictive biomarkers and obtaining their importance rankings. Moreover, the package provides graphical plots tailored for biomarker analysis. This tool enables clinical researchers seeking to enhance their understanding of biomarkers and patient population in drug development. AVAILABILITY AND IMPLEMENTATION: The package is available at CRAN and https://github.com/deeplearner0731/BioPred.
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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.