Evidence map›Paper›PMID 35642895›Full record

ArticleBioinformatics (Oxford, England)2022

MIO: microRNA target analysis system for immuno-oncology.

Pablo Monfort-Lanzas, Raphael Gronauer, Leonie Madersbacher, Christoph Schatz, Dietmar Rieder, Hubert Hackl

Open access · hybridAbstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 56% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. Modeling omics dose-response at the pathway level with DoseRider.Computational and structural biotechnology journal · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 1 institution in 1 country.

Pablo Monfort-LanzasInstitute of Bioinformatics, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Raphael GronauerInstitute of Bioinformatics, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Leonie MadersbacherInstitute of Bioinformatics, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Christoph SchatzInstitute of Pathology, Neuropathology and Molecular Pathology, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Dietmar RiederInstitute of Bioinformatics, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Hubert HacklInstitute of Bioinformatics, Biocenter, Medical University of Innsbruck, 6020 Innsbruck, Austria.ORCID 0000-0003-4055-3841
Innsbruck Medical University · AT

Funding

Austrian Science Fund P34783National Bank of Austria 18279University of Valencia
6 · The paper itself

Abstract

summaryMicroRNAs have been shown to be able to modulate the tumor microenvironment and the immune response and hence could be interesting biomarkers and therapeutic targets in immuno-oncology; however, dedicated analysis tools are missing. Here, we present a user-friendly web platform MIO and a Python toolkit miopy integrating various methods for visualization and analysis of provided or custom bulk microRNA and gene expression data. We include regularized regression and survival analysis and provide information of 40 microRNA target prediction tools as well as a collection of curated immune related gene and microRNA signatures and processed TCGA data including estimations of infiltrated immune cells and the immunophenoscore. The integration of several machine learning methods enables the selection of prognostic and predictive microRNAs and gene interaction network biomarkers. AVAILABILITY AND IMPLEMENTATION: https://mio.icbi.at, https://github.com/icbi-lab/mio and https://github.com/icbi-lab/miopy. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

MicroRNAsNeoplasmsGene Regulatory NetworksHumansMachine LearningSoftwareTumor MicroenvironmentMicroRNAs

Identifiers

PMID35642895
PMCPMC9272810
OpenAlexW4281875629

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.