Evidence map›Paper›PMID 30175097›Full record

ReviewFrontiers in cell and developmental biology2018

What We Learned From Big Data for Autophagy Research.

Anne-Claire Jacomin, Lejla Gul, Padhmanand Sudhakar, Tamas Korcsmaros, Ioannis P Nezis

Abstract readReview
In one paragraph

Review in Frontiers in cell and developmental biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

  1. Article
  2. When autophagy meets placenta development and pregnancy complications.Frontiers in cell and developmental biology · 2024
    Review
  3. Article
  4. Review
  5. Review
  6. Article
  7. Shedding Light on Autophagy During Human Tuberculosis. A Long Way to Go.Frontiers in cellular and infection microbiology · 2021
    Review
  8. Article
  9. 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

5 authors.

Anne-Claire JacominSchool of Life Sciences, University of Warwick, Coventry, United Kingdom.
Lejla GulEarlham Institute, Norwich Research Park, Norwich, United Kingdom.
Padhmanand SudhakarEarlham Institute, Norwich Research Park, Norwich, United Kingdom.
Tamas KorcsmarosEarlham Institute, Norwich Research Park, Norwich, United Kingdom.
Ioannis P NezisSchool of Life Sciences, University of Warwick, Coventry, United Kingdom.

Funding

Biotechnology and Biological Sciences Research Council BBS/E/F/000PR10353Biotechnology and Biological Sciences Research Council BBS/E/F/000PR10355Biotechnology and Biological Sciences Research Council BBS/E/T/000PR9817
6 · The paper itself

Abstract

Autophagy is the process by which cytoplasmic components are engulfed in double-membraned vesicles before being delivered to the lysosome to be degraded. Defective autophagy has been linked to a vast array of human pathologies. The molecular mechanism of the autophagic machinery is well-described and has been extensively investigated. However, understanding the global organization of the autophagy system and its integration with other cellular processes remains a challenge. To this end, various bioinformatics and network biology approaches have been developed by researchers in the last few years. Recently, large-scale multi-omics approaches (like genomics, transcriptomics, proteomics, lipidomics, and metabolomics) have been developed and carried out specifically focusing on autophagy, and generating multi-scale data on the related components. In this review, we outline recent applications of

Indexed as

autophagybig databioinformaticsproteomicstranscriptomics

Identifiers

PMID30175097
PMCPMC6107789

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.