miTALOS: Analyzing the tissue-specific regulation of signaling pathways by human and mouse microRNAs is a research paper published in RNA (2011). On theSindex it has a DataRank of 0.590. It has been cited 50 times.
MicroRNAs (miRNAs) are an important class of post-transcriptional regulators of gene expression that are involved in various cellular and phenotypic processes. A number of studies have shown that miRNA expression is induced by signaling pathways. Moreover, miRNAs emerge as regulators of signaling pathways. Here, we present the miTALOS web resource, which provides insight into miRNA-mediated regulation of signaling pathways. As a novel feature, miTALOS considers the tissue-specific expression signatures of miRNAs and target transcripts to improve the analysis of miRNA regulation in biological pathways. MiTALOS identifies potential pathway regulation by (i) an enrichment analysis of miRNA targets genes and (ii) by using a proximity score to evaluate the functional role of miRNAs in biological pathways by their network proximity. Moreover, miTALOS integrates five different miRNA target prediction tools and two different signaling pathway resources (KEGG and NCI). A graphical visualization of miRNA targets in both KEGG and NCI PID signaling pathways is provided to illustrate their respective pathway context. We perform a functional analysis on prostate cancer-related miRNAs and are able to infer a model of miRNA-mediated regulation on tumor proliferation, mobility and anti-apoptotic behavior. miTALOS provides novel features that accomplish a substantial support to systematically infer regulation of signaling pathways mediated by miRNAs. The web-server is freely accessible at http://hmgu.de/cmb/mitalos.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.590
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.