Evidence map›Paper›PMID 34432273›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2022

Functional Annotation of MicroRNAs Using Existing Resources.

Harsh Dweep, Louise C Showe, Andrew V Kossenkov

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 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
1.2field-weighted citation impact, top 18% 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, 4 citations in OpenAlex.

  1. 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

3 authors at 1 institution in 1 country.

Harsh DweepThe Wistar Institute, Philadelphia, PA, USA. hdweep@wistar.org.
Louise C ShoweThe Wistar Institute, Philadelphia, PA, USA.
Andrew V KossenkovThe Wistar Institute, Philadelphia, PA, USA.
The Wistar Institute · US

Funding

Tumor Microenvironment and MetastasisP30CA010815 · NCI · WISTAR INSTITUTE · PI Luis J Montaner · 1985 to 2026
$75.9M
Integration of Biomarker Signatures from Peripheral Blood for Diagnosis, Prognosis, Remission and Recurrence of Lung CancerU01CA200495 · NCI · WISTAR INSTITUTE · PI LIU, QIN, SHOWE, LOUISE C. · 2016 to 2021
$4.0M
Integrative Approach to Comprehensive Analysis of High Throughput Data on a Cancer Center LevelR50CA211199 · NCI · WISTAR INSTITUTE · PI Andrew V Kossenkov · 2016 to 2026
$1.6M
NCI NIH HHS P30 CA010815NCI NIH HHS R50 CA211199NCI NIH HHS U01 CA200495
6 · The paper itself

Abstract

MicroRNAs (miRNAs) are endogenous small noncoding RNAs that are involved in most biological signaling pathways, including the cell cycle, apoptosis, proliferation, immune response, metabolism as well as in biological processes including organ development and in human diseases like cancers. During the past two decades, high-throughput transcriptomic profiling using next generation sequencing and microarrays have been extensively utilized to identify differentially expressed miRNAs across different conditions and diseases. A natural extension of miRNA identification is to the process of functionally annotating known or predicted gene targets of those miRNAs and, by inference, revealing their potential influences on diverse biological pathways and functions. In this chapter, we provide a stepwise guideline on how to perform functional enrichment analyses on miRNAs of interest using publicly available resources such as miRWalk2.0.

Indexed as

Gene Expression ProfilingHigh-Throughput Nucleotide SequencingHumansMicroRNAsMicroRNAsAnalysisDatabasesDiseasesFunctionsMicroRNAsmiRWalk2.0PathwaysPredictionValidationWorkflow

Identifiers

PMID34432273
OpenAlexW3193897202

What OpenQuestion holds

Textmetadata
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.