The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method
Open accessAbstract
Motivation: We recently introduced a multivariate approach that selects a subset of predictive genes jointly for sample classification based on expression data. We tested the algorithm on colon and leukemia data sets. As an extension to our earlier work, we systematically examine the sensitivity, reproducibility and stability of gene selection/sample classification to the choice of parameters of the algorithm. Methods: Our approach combines a Genetic Algorithm (GA) and the k-Nearest Neighbor (KNN) method to identify genes that can jointly discriminate between different classes of samples (e.g. normal versus tumor). The GA/KNN method is a stochastic supervised pattern recognition method. The genes identified are subsequently used to classify independent test set samples. Results: The GA/KNN method is capable of selecting a subset of predictive genes from a large noisy data set for sample classification. It is a multivariate approach that can capture the correlated structure in the data. We find that for a given data set gene selection is highly repeatable in independent runs using the GA/KNN method. In general, however, gene selection may be less robust than classification. Availability: The method is available at http://dir.niehs.nih.gov/microarray/datamining Contact: LI3@niehs.nih.gov * To whom correspondence should be addressed.
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
Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,010
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,002
A decision-theoretic generalization of on-line learning and an application to boosting
Freund, Y., & Schapire, R. E. (1997) · Journal of Computer and System Sciences · doi:10.1006/jcss.1997.1504 · Cited by 20,466
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Cluster analysis and display of genome-wide expression patterns
Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.25.14863 · Cited by 16,184
Goldberg, D. E., & Shakespeare, W. (2002) · Cited by 16,088
Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J. P., Coller, H., Loh, M. L., Downing, J. R., Caligiuri, M. A., et al. (1999) · Science · doi:10.1126/science.286.5439.531 · Cited by 11,516
Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling
Alizadeh, A. A., Eisen, M. B., Davis, R. E., Ma, C., Lossos, I. S., Rosenwald, A., Boldrick, J. C., Sabet, H., Tran, T., Yu, X., et al. (2000) · Nature · doi:10.1038/35000501 · Cited by 9,925
Alon, U., Barkai, N., Notterman, D. A., Gish, K., Ybarra, S., Mack, D., & Levine, A. J. (1999) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.96.12.6745 · Cited by 4,167
Clustering Gene Expression Patterns
Ben-Dor, A., Shamir, R., & Yakhini, Z. (1999) · Journal of Computational Biology · doi:10.1089/106652799318274 · Cited by 1,203
Cited by
Text Classification Algorithms: A Survey
Kowsari, K., Jafari Meimandi, K., Heidarysafa, M., Mendu, S., Barnes, L., & Brown, D. (2019) · Information · doi:10.3390/info10040150 · Cited by 1,312
Cluster analysis for gene expression data: a survey
Jiang, D., Tang, C., & Zhang, A. (2004) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2004.68 · Cited by 1,235
Machine learning in bioinformatics
Larrañaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J. A., Armañanzas, R., Santafé, G., Pérez, A., et al. (2006) · Briefings in Bioinformatics · doi:10.1093/bib/bbk007 · Cited by 867
Markov blanket-embedded genetic algorithm for gene selection
Zhu, Z., Ong, Y.-S., & Dash, M. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.02.007 · Cited by 494
Aliferis, C., Statnikov, A., Tsamardinos, I., Mani, S., & Koutsoukos, X. (2010) · doi:10.5555/1756006.1756013 · Cited by 469
Metadata
- Authors
- Li, L., Weinberg, C. R., Darden, T. A., & Pedersen, L. G.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-12-01
- Volume
- 17
- Issue
- 12
- Pages
- 1131-1142
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2127544153
- Cited by
- 638
- References
- 14
- FWCI
- 8.03
- 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: Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. https://app.cochat.ai/papers/10.1093/bioinformatics/17.12.1131
- The paper
- Li, L., Weinberg, C. R., Darden, T. A., & Pedersen, L. G. (2001). Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics. https://doi.org/10.1093/bioinformatics/17.12.1131
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.
