The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Advances and Challenges in Computational Prediction of Effectors from Plant Pathogenic Fungi
Open accessAbstract
1 CSIRO Agriculture Flagship, Centre for Environment and Life Sciences, Perth, Western Australia, Australia, 2 CSIRO Agriculture Flagship, Black Mountain Laboratories, Canberra, Australian Capital Territory, Australia, 3 CSIRO Agriculture Flagship, Queensland Bioscience Precinct, Brisbane, Queensland, Australia, 4 University of Western Australia Institute of Agriculture, University of Western Australia, Crawley, Western Australia, Australia
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.
Integrity signals
Registry
Cites
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.
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
SignalP 4.0: discriminating signal peptides from transmembrane regions
Petersen, T. N., Brunak, S., von Heijne, G., & Nielsen, H. (2011) · Nature Methods · doi:10.1038/nmeth.1701 · Cited by 8,847
Cited by
LOCALIZER: subcellular localization prediction of both plant and effector proteins in the plant cell
Sperschneider, J., Catanzariti, A.-M., DeBoer, K., Petre, B., Gardiner, D. M., Singh, K. B., Dodds, P. N., & Taylor, J. M. (2017) · Scientific Reports · doi:10.1038/srep44598 · Cited by 466
Self-citationPrediction of effector protein structures from fungal phytopathogens enables evolutionary analyses
(2023) · doi:10.1038/s41564-022-01287-6 · Cited by 215
Gan, P., Narusaka, M., Kumakura, N., Tsushima, A., Takano, Y., Narusaka, Y., & Shirasu, K. (2016) · Genome Biology and Evolution · doi:10.1093/gbe/evw089 · Cited by 98
Metadata
- Authors
- Sperschneider, J., Dodds, P. N., Gardiner, D. M., Manners, J. M., Singh, K. B., & Taylor, J. M.
- Venue
- PLOS Pathogens
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-05-28
- Volume
- 11
- Issue
- 5
- Pages
- e1004806
- ISSN
- 1553-7366 (ISSN-L)
- 1553-7374
- OpenAlex
- W374729652
- Cited by
- 176
- References
- 1
- FWCI
- 10.33
- Type
- journal article
- Language
- en
- Open access
- gold
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: Advances and Challenges in Computational Prediction of Effectors from Plant Pathogenic Fungi. https://app.cochat.ai/papers/10.1371/journal.ppat.1004806
- The paper
- Sperschneider, J., Dodds, P. N., Gardiner, D. M., Manners, J. M., Singh, K. B., & Taylor, J. M. (2015). Advances and Challenges in Computational Prediction of Effectors from Plant Pathogenic Fungi. PLOS Pathogens. https://doi.org/10.1371/journal.ppat.1004806
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.
