Evidence map›Paper›PMID 35167085›Full record

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

Exploration of Protein Aggregations in Parkinson's Disease Through Computational Approaches and Big Data Analytics.

Saba Shahzadi, Muhammad Yasir, Bisma Aftab, Sumbal Babar, Mubashir Hassan

Abstract read
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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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
3.8field-weighted citation impact, top 7% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. Prediction and Evaluation of Protein Aggregation with Computational Methods.Methods in molecular biology (Clifton, N.J.) · 2025
    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

5 authors at 3 institutions in 2 countries.

Saba ShahzadiInstitute of Molecular Sciences and Bioinformatics, Lahore, Pakistan.
Muhammad YasirInstitute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan.
Bisma AftabInstitute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan.
Sumbal BabarInstitute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan.
Mubashir HassanInstitute of Molecular Biology and Biotechnology, The University of Lahore, Lahore, Pakistan. mubashirhassan_gcul@yahoo.com.
University of Lahore · PKInstitute of Molecular Sciences and Bioinformatics · PKNationwide Children's Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Protein aggregation has been implicated in numerous neurodegenerative disorders whose etiologies are poorly understood, and for which there are no effective treatments. Here we show that the computational approaches may help us to better understand the basics of Parkinson's disease (PD). The high-resolution structural, dynamical, and mechanistic insights delivered by computational studies of protein aggregation have a unique potential to enable the rational manipulation of oligomer formation. Additionally, big data and machine learning methods may provide valuable insights to better understand the nature of proteins involved in PD and their aggregative behavior for the betterment of PD treatment.

Indexed as

Parkinson DiseaseBig DataData ScienceHumansMachine LearningProtein AggregatesProtein AggregatesBig dataParkinson’s diseaseProtein aggregation

Identifiers

PMID35167085
OpenAlexW4212787123

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.