Evidence map›Paper›PMID 38033382›Full record

ArticleBioinformatics and biology insights2023

A Comprehensive Bioinformatics Approach to Identify Molecular Signatures and Key Pathways for the Huntington Disease.

Tahera Mahnaz Meem, Umama Khan, Md Bazlur Rahman Mredul, Md Abdul Awal, Md Habibur Rahman, Md Salauddin Khan

Open access · goldAbstract read
In one paragraph

Article in Bioinformatics and biology insights, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.5field-weighted citation impact, top 19% 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

6 citing papers in PubMed, 10 citations in OpenAlex.

  1. Review
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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 2 institutions in 1 country.

Tahera Mahnaz MeemStatistics Discipline, Science, Engineering & Technology School, Khulna University, Khulna, Bangladesh.
Umama KhanBiotechnology & Genetic Engineering Discipline, Khulna University, Khulna, Bangladesh.
Md Bazlur Rahman MredulStatistics Discipline, Science, Engineering & Technology School, Khulna University, Khulna, Bangladesh.
Md Abdul AwalElectronics and Communication Engineering Discipline, Khulna University, Khulna, Bangladesh.
Md Habibur RahmanDepartment of Computer Science and Engineering, Islamic University, Kushtia, Bangladesh.
Md Salauddin KhanStatistics Discipline, Science, Engineering & Technology School, Khulna University, Khulna, Bangladesh.
Khulna University · BDIslamic University · BD

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Huntington disease (HD) is a degenerative brain disease caused by the expansion of CAG (cytosine-adenine-guanine) repeats, which is inherited as a dominant trait and progressively worsens over time possessing threat. Although HD is monogenetic, the specific pathophysiology and biomarkers are yet unknown specifically, also, complex to diagnose at an early stage, and identification is restricted in accuracy and precision. This study combined bioinformatics analysis and network-based system biology approaches to discover the biomarker, pathways, and drug targets related to molecular mechanism of HD etiology. The gene expression profile data sets GSE64810 and GSE95343 were analyzed to predict the molecular markers in HD where 162 mutual differentially expressed genes (DEGs) were detected. Ten hub genes among them (

Indexed as

bioinformaticsbiomarkershub genesHuntington diseasepathwaysystem biology

Identifiers

PMID38033382
PMCPMC10683407
OpenAlexW4389049798

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.