The DOI matches its Crossref record; 100 of the 100 references it cites were found in a registry or an index.
mirDIP 4.1—integrative database of human microRNA target predictions
Open accessAbstract
MicroRNAs are important regulators of gene expression, achieved by binding to the gene to be regulated. Even with modern high-throughput technologies, it is laborious and expensive to detect all possible microRNA targets. For this reason, several computational microRNA-target prediction tools have been developed, each with its own strengths and limitations. Integration of different tools has been a successful approach to minimize the shortcomings of individual databases. Here, we present mirDIP v4.1, providing nearly 152 million human microRNA-target predictions, which were collected across 30 different resources. We also introduce an integrative score, which was statistically inferred from the obtained predictions, and was assigned to each unique microRNA-target interaction to provide a unified measure of confidence. We demonstrate that integrating predictions across multiple resources does not cumulate prediction bias toward biological processes or pathways. mirDIP v4.1 is freely available at http://ophid.utoronto.ca/mirDIP/.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters
Yu, G., Wang, L.-G., Han, Y., & He, Q.-Y. (2012) · OMICS: A Journal of Integrative Biology · doi:10.1089/omi.2011.0118 · Cited by 36,259
Measures of the Amount of Ecologic Association Between Species
Dice, L. R. (1945) · Ecology · doi:10.2307/1932409 · Cited by 11,874
Predicting effective microRNA target sites in mammalian mRNAs
Agarwal, V., Bell, G. W., Nam, J.-W., & Bartel, D. P. (2015) · eLife · doi:10.7554/elife.05005 · Cited by 7,318
A ceRNA Hypothesis: The Rosetta Stone of a Hidden RNA Language?
Salmena, L., Poliseno, L., Tay, Y., Kats, L., & Pandolfi, P. P. (2011) · Cell · doi:10.1016/j.cell.2011.07.014 · Cited by 7,262
Regulation of microRNA biogenesis
Ha, M., & Kim, V. N. (2014) · Nature Reviews Molecular Cell Biology · doi:10.1038/nrm3838 · Cited by 5,462
miRBase: annotating high confidence microRNAs using deep sequencing data
Kozomara, A., & Griffiths-Jones, S. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1181 · Cited by 5,073
Combinatorial microRNA target predictions
Krek, A., Grün, D., Poy, M. N., Wolf, R., Rosenberg, L., Epstein, E. J., MacMenamin, P., da Piedade, I., Gunsalus, K. C., Stoffel, M., et al. (2005) · Nature Genetics · doi:10.1038/ng1536 · Cited by 4,603
Fast and effective prediction of microRNA/target duplexes
REHMSMEIER, M., STEFFEN, P., HÖCHSMANN, M., & GIEGERICH, R. (2004) · RNA · doi:10.1261/rna.5248604 · Cited by 2,560
The role of site accessibility in microRNA target recognition
Kertesz, M., Iovino, N., Unnerstall, U., Gaul, U., & Segal, E. (2007) · Nature Genetics · doi:10.1038/ng2135 · Cited by 2,460
Gene silencing by microRNAs: contributions of translational repression and mRNA decay
Huntzinger, E., & Izaurralde, E. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2936 · Cited by 2,339
Cited by
Trials and Tribulations of MicroRNA Therapeutics
Seyhan, A. A. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms25031469 · Cited by 381
miRTargetLink 2.0—interactive miRNA target gene and target pathway networks
Kern, F., Aparicio-Puerta, E., Li, Y., Fehlmann, T., Kehl, T., Wagner, V., Ray, K., Ludwig, N., Lenhof, H.-P., Meese, E., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkab297 · Cited by 205
Hepatic macrophages act as a central hub for relaxin-mediated alleviation of liver fibrosis
Hu, M., Wang, Y., Liu, Z., Yu, Z., Guan, K., Liu, M., Wang, M., Tan, J., & Huang, L. (2021) · Nature Nanotechnology · doi:10.1038/s41565-020-00836-6 · Cited by 136
Alizadeh Savareh, B., Asadzadeh Aghdaie, H., Behmanesh, A., Bashiri, A., Sadeghi, A., Zali, M., & Shams, R. (2020) · Pancreatology · doi:10.1016/j.pan.2020.07.399 · Cited by 74
Inhibition of microRNA-128-3p attenuates hypercholesterolemia in mouse model
Chandra, A., Sharma, K., Pratap, K., Singh, V., & Saini, N. (2021) · Life Sciences · doi:10.1016/j.lfs.2020.118633 · Cited by 19
Metadata
- Authors
- Tokar, T., Pastrello, C., Rossos, A. E. M., Abovsky, M., Hauschild, A.-C., Tsay, M., Lu, R., & Jurisica, I.
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-11-29
- Volume
- 46
- Issue
- D1
- Pages
- D360-D370
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- OpenAlex
- W2768781242
- Cited by
- 572
- References
- 100
- FWCI
- 10.89
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: mirDIP 4.1—integrative database of human microRNA target predictions. https://app.cochat.ai/papers/10.1093/nar/gkx1144
- The paper
- Tokar, T., Pastrello, C., Rossos, A. E. M., Abovsky, M., Hauschild, A.-C., Tsay, M., Lu, R., & Jurisica, I. (2018). mirDIP 4.1—integrative database of human microRNA target predictions. Nucleic Acids Research. https://doi.org/10.1093/nar/gkx1144
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
