Evidence map›Paper›PMID 33326066›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2021

Bioinformatics Approaches for Functional Prediction of Long Noncoding RNAs.

Fayaz Seifuddin, Mehdi Pirooznia

Abstract read
PubMed Publisher
In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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

2 citing papers in PubMed.

  1. Bioinformatics Approaches in Noncoding RNAs Research.Experientia supplementum (2012) · 2026
    Review
  2. Bioinformatic Tools for the Analysis and Prediction of ncRNA Interactions.International journal of molecular sciences · 2021
    Review
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

2 authors.

Fayaz SeifuddinBioinformatics and Computational Biology, National Heart, Lung, and Blood Institute National Institutes of Health, Bethesda, MD, USA.
Mehdi PiroozniaBioinformatics and Computational Biology, National Heart, Lung, and Blood Institute National Institutes of Health, Bethesda, MD, USA. mehdi.pirooznia@nih.gov.

Funding

Bioinformatics CoreZICHL006228 · NHLBI · NATIONAL HEART, LUNG, AND BLOOD INSTITUTE · PI PIROOZNIA, MEHDI · 2017 to 2021
$6.6M
6 · The paper itself

Abstract

There is accumulating evidence that long noncoding RNAs (lncRNAs) play crucial roles in biological processes and diseases. In recent years, computational models have been widely used to predict potential lncRNA-disease relations. In this chapter, we systematically describe various computational algorithms and prediction tools that have been developed to elucidate the roles of lncRNAs in diseases, coding potential/functional characterization, or ascertaining their involvement in critical biological processes as well as provide a comprehensive summary of these applications.

Indexed as

AlgorithmsComputational BiologyDiseaseHumansProteinsRNA, Long NoncodingProteinsRNA, Long NoncodingLncRNA BioinformaticsLncRNA coding potentialLncRNA–disease associationLncRNA functional predictionLncRNA–protein correlation

Identifiers

PMID33326066

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.