Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 416

The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.

Visual account of protein investment in cellular functions

Open access
Liebermeister, Wolfram, Noor, Elad, Flamholz, Avi, Davidi, Dan, Bernhardt, Jörg, Milo, Ron·2014·Proceedings of the National Academy of Sciences

Abstract

Significance: Proteins, which constitute roughly half of the cell dry mass, are extremely diverse. By counting the protein copy number of each gene in the genome, we obtain the proteome—a comprehensive picture of a cell's biochemical machinery. The proteome reflects physiology, structure, metabolic capacities, and many other aspects of the cell's lifestyle. Here, we visualize quantitative proteome data using a graphical tool we call proteomaps, where proteins are shown as polygons whose sizes indicate the abundances. Proteins involved in similar cellular functions are arranged in adjacent locations, creating regions whose areas give insight into the relative investment in each functional class. Proteins or protein classes that dominate the proteomap indicate demanding cellular processes and promising targets for further research.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1073/pnas.1314810111

Cites

33 of 33 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 33 references

Cited by

See all 416

Metadata

Authors
Liebermeister, W., Noor, E., Flamholz, A., Davidi, D., Bernhardt, J., & Milo, R.
Venue
Proceedings of the National Academy of Sciences
Published
2014-06-02
Volume
111
Issue
23
Pages
8488-8493
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W2018003897
Cited by
416
References
33
FWCI
4.38
Type
journal article
Language
en
Open access
bronze

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Visual account of protein investment in cellular functions. https://app.cochat.ai/papers/10.1073/pnas.1314810111
The paper
Liebermeister, W., Noor, E., Flamholz, A., Davidi, D., Bernhardt, J., & Milo, R. (2014). Visual account of protein investment in cellular functions. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1314810111

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.