Evidence map›Paper›PMID 37787768›Full record

ArticleeLife2023

Broad functional profiling of fission yeast proteins using phenomics and machine learning.

María Rodríguez-López, Nicola Bordin, Jon Lees, Harry Scholes, Shaimaa Hassan, Quentin Saintain, Stephan Kamrad, Christine Orengo, Jürg Bähler

Open access · goldAbstract read
In one paragraph

Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
3.4field-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

26 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. The power of many: when genetics met yeasts and high-throughput.Biological reviews of the Cambridge Philosophical Society · 2026
    Review
  3. Article
  4. Humanization of theGenes · 2026
    Article
  5. Article
  6. Defining thebioRxiv : the preprint server for biology · 2026
    Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. TheCells · 2025
    Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Article
  19. Article
  20. 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

9 authors at 2 institutions in 2 countries.

María Rodríguez-López *University College London, Institute of Healthy Ageing and Department of Genetics, Evolution & Environment, London, United Kingdom.ORCID 0000-0002-2066-0589
Nicola Bordin *University College London, Institute of Structural and Molecular Biology, London, United Kingdom.ORCID 0000-0002-6568-9035
Jon LeesUniversity College London, Institute of Structural and Molecular Biology, London, United Kingdom.ORCID 0000-0003-3925-1424
Harry ScholesUniversity College London, Institute of Structural and Molecular Biology, London, United Kingdom.
Shaimaa HassanUniversity College London, Institute of Healthy Ageing and Department of Genetics, Evolution & Environment, London, United Kingdom.
Quentin SaintainUniversity College London, Institute of Healthy Ageing and Department of Genetics, Evolution & Environment, London, United Kingdom.
Stephan KamradUniversity College London, Institute of Healthy Ageing and Department of Genetics, Evolution & Environment, London, United Kingdom.
Christine OrengoUniversity College London, Institute of Structural and Molecular Biology, London, United Kingdom.ORCID 0000-0002-7141-8936
Jürg BählerUniversity College London, Institute of Healthy Ageing and Department of Genetics, Evolution & Environment, London, United Kingdom.ORCID 0000-0003-4036-1532
Institute of Structural and Molecular Biology · GBUniversity of Bristol · GB

Funding

Biotechnology and Biological Sciences Research Council BB/R009597/1Medical Research Council MR/W003309/1Medical Research Council MR/W003309/2
6 · The paper itself

Abstract

Many proteins remain poorly characterized even in well-studied organisms, presenting a bottleneck for research. We applied phenomics and machine-learning approaches with

Indexed as

SchizosaccharomycesSchizosaccharomyces pombe ProteinsHumansMachine LearningPhenomicsPhenotypeSchizosaccharomyces pombe Proteinscell phenotypecomputational biologycomputational predictionfunctional genomicsgene ontologygeneticsgenomicsmachine learningS. pombesystems biologyunknown protein

Identifiers

PMID37787768
PMCPMC10547477
OpenAlexW4378837537

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.