The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Disambiguating proteins, genes, and RNA in text: a machine learning approach
Open accessAbstract
We present an automated system for assigning protein, gene, or mRNA class labels to biological terms in free text. Three machine learning algorithms and several extended ways for defining contextual features for disambiguation are examined, and a fully unsupervised manner for obtaining training examples is proposed. We train and evaluate our system over a collection of 9 million words of molecular biology journal articles, obtaining accuracy rates up to 85%. Contact: vh@cs.columbia.edu
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
Applied Bayesian and Classical Inference
Mosteller, F., & Wallace, D. L. (1984) · Springer Series in Statistics · doi:10.1007/978-1-4612-5256-6 · Cited by 195
Cited by
The text mining handbook: advanced approaches in analyzing unstructured data
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5684 · Cited by 1,598
Friedman, C., Kra, P., Yu, H., Krauthammer, M., & Rzhetsky, A. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.suppl_1.s74 · Cited by 507
Self-citationThe challenge and prospect of mRNA therapeutics landscape
Weng, Y., Li, C., Yang, T., Hu, B., Zhang, M., Guo, S., Xiao, H., Liang, X.-J., & Huang, Y. (2020) · Biotechnology Advances · doi:10.1016/j.biotechadv.2020.107534 · Cited by 417
Text mining and its potential applications in systems biology
Ananiadou, S., Kell, D. B., & Tsujii, J.-I. (2006) · Trends in Biotechnology · doi:10.1016/j.tibtech.2006.10.002 · Cited by 324
Information retrieval and text mining technologies for chemistry
Krallinger, M., Rabal, O., Lourenço, A., Oyarzabal, J., & Valencia, A. (2017) · Chemical Reviews · doi:10.1021/acs.chemrev.6b00851 · Cited by 281
Metadata
- Authors
- Hatzivassiloglou, V., Duboué, P. A., & Rzhetsky, A.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-06-01
- Volume
- 17
- Issue
- suppl_1
- Pages
- S97-S106
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2134769633
- Cited by
- 224
- References
- 1
- FWCI
- 6.95
- Type
- journal article
- Language
- en
- Open access
- green
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: Disambiguating proteins, genes, and RNA in text: a machine learning approach. https://app.cochat.ai/papers/10.1093/bioinformatics/17.suppl_1.s97
- The paper
- Hatzivassiloglou, V., Duboué, P. A., & Rzhetsky, A. (2001). Disambiguating proteins, genes, and RNA in text: a machine learning approach. Bioinformatics. https://doi.org/10.1093/bioinformatics/17.suppl_1.s97
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.
