ArticleBioinformatics (Oxford, England)2018
JAMI: fast computation of conditional mutual information for ceRNA network analysis.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Modeling ncRNA Synergistic Regulation in Cancer.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Identification of aberrantly expressed lncRNAs and ceRNA networks in multiple myeloma: a combined high-throughput sequencing and microarray analysis.Frontiers in oncology · 2023Article
- ceRNAR: An R package for identification and analysis of ceRNA-miRNA triplets.PLoS computational biology · 2022Article
- MethReg: estimating the regulatory potential of DNA methylation in gene transcription.Nucleic acids research · 2022Article
- Article
- Illuminating lncRNA Function Through Target Prediction.Methods in molecular biology (Clifton, N.J.) · 2021Article
- Identification of Potential Prognostic Competing Triplets in High-Grade Serous Ovarian Cancer.Frontiers in genetics · 2020Article
- Large-scale inference of competing endogenous RNA networks with sparse partial correlation.Bioinformatics (Oxford, England) · 2019Article
- miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules.BMC bioinformatics · 2019Article
- Integrated Bioinformatic Analysis of a Competing Endogenous RNA Network Reveals a Prognostic Signature in Endometrial Cancer.Frontiers in oncology · 2019Article
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Motivation: Genome-wide measurements of paired miRNA and gene expression data have enabled the prediction of competing endogenous RNAs (ceRNAs). It has been shown that the sponge effect mediated by protein-coding as well as non-coding ceRNAs can play an important regulatory role in the cell in health and disease. Therefore, many computational methods for the computational identification of ceRNAs have been suggested. In particular, methods based on Conditional Mutual Information (CMI) have shown promising results. However, the currently available implementation is slow and cannot be used to perform computations on a large scale. Results: Here, we present JAMI, a Java tool that uses a non-parametric estimator for CMI values from gene and miRNA expression data. We show that JAMI speeds up the computation of ceRNA networks by a factor of ∼70 compared to currently available implementations. Further, JAMI supports multi-threading to make use of common multi-core architectures for further performance gain. Requirements: Java 8. Availability and implementation: JAMI is available as open-source software from https://github.com/SchulzLab/JAMI. Supplementary information: Supplementary data are available at Bioinformatics online.
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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.