ArticleBioinformatics (Oxford, England)2020
PUMA: PANDA Using MicroRNA Associations.
Article in Bioinformatics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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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Who cites it
18 citing papers in PubMed, 34 citations in OpenAlex.
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- Data-driven universal insights into tumorigenesis via hallmark networks.NPJ systems biology and applications · 2025Article
- Critical analysis of descriptive microRNA data in the translational research on cardioprotection and cardiac repair: lost in the complexity of bioinformatics.Basic research in cardiology · 2025Review
- miRNA-mRNA network analysis identifies PAX5 as a potential regulator of adaptive immune response in COPD.bioRxiv : the preprint server for biology · 2025Article
- Advanced computational predictive models of miRNA-mRNA interaction efficiency.Computational and structural biotechnology journal · 2024Article
- Predicting the Effect of miRNA on Gene Regulation to Foster Translational Multi-Omics Research-A Review on the Role of Super-Enhancers.Non-coding RNA · 2024Review
- Use of microRNAs as Diagnostic, Prognostic, and Therapeutic Tools for Glioblastoma.International journal of molecular sciences · 2024Review
- Methods for multi-omic data integration in cancer research.Frontiers in genetics · 2024Review
- Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data.Bioinformatics (Oxford, England) · 2023Article
- The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.Genome biology · 2023Article
- gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit.NAR genomics and bioinformatics · 2022Article
- The use of machine learning to discover regulatory networks controlling biological systems.Molecular cell · 2022Review
- GRAND: a database of gene regulatory network models across human conditions.Nucleic acids research · 2022Article
- Functional impact of multi-omic interactions in breast cancer subtypes.Frontiers in genetics · 2022Article
- Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases.Frontiers in cardiovascular medicine · 2022Review
- Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme.Cancer research · 2021Article
- Community Detection in Large-Scale Bipartite Biological Networks.Frontiers in genetics · 2021Review
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
motivationConventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA-target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue. AVAILABILITY AND IMPLEMENTATION: PUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.