The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
So you think you can PLS-DA?
Open accessAbstract
Background: Partial Least-Squares Discriminant Analysis (PLS-DA) is a popular machine learning tool that is gaining increasing attention as a useful feature selector and classifier. In an effort to understand its strengths and weaknesses, we performed a series of experiments with synthetic data and compared its performance to its close relative from which it was initially invented, namely Principal Component Analysis (PCA). Results: We demonstrate that even though PCA ignores the information regarding the class labels of the samples, this unsupervised tool can be remarkably effective as a feature selector. In some cases, it outperforms PLS-DA, which is made aware of the class labels in its input. Our experiments range from looking at the signal-to-noise ratio in the feature selection task, to considering many practical distributions and models encountered when analyzing bioinformatics and clinical data. Other methods were also evaluated. Finally, we analyzed an interesting data set from 396 vaginal microbiome samples where the ground truth for the feature selection was available. All the 3D figures shown in this paper as well as the supplementary ones can be viewed interactively at http://biorg.cs.fiu.edu/plsda Conclusions: Our results highlighted the strengths and weaknesses of PLS-DA in comparison with PCA for different underlying data models.
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Cites
Independent Component Analysis
(2004) · Independent Component Analysis · doi:10.7551/mitpress/3717.003.0014 · Cited by 7,903
Independent Component Analysis
Hyvärinen, A., Karhunen, J., & Oja, E. (2001) · Wiley eBooks · doi:10.1002/0471221317 · Cited by 5,007
Vaginal microbiome of reproductive-age women
Ravel, J., Gajer, P., Abdo, Z., Schneider, G. M., Koenig, S. S. K., McCulle, S. L., Karlebach, S., Gorle, R., Russell, J., Tacket, C. O., et al. (2011) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1002611107 · Cited by 4,254
mixOmics: An R package for ‘omics feature selection and multiple data integration
Rohart, F., Gautier, B., Singh, A., & Lê Cao, K.-A. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005752 · Cited by 4,056
Partial least squares for discrimination
Barker, M., & Rayens, W. (2003) · Journal of Chemometrics · doi:10.1002/cem.785 · Cited by 2,841
Regularized Discriminant Analysis
Friedman, J. H. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478752 · Cited by 2,421
Temporal Dynamics of the Human Vaginal Microbiota
Gajer, P., Brotman, R. M., Bai, G., Sakamoto, J., Schütte, U. M. E., Zhong, X., Koenig, S. S. K., Fu, L., Ma, Z. (., Zhou, X., et al. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3003605 · Cited by 1,558
Selection bias in gene extraction on the basis of microarray gene-expression data
Ambroise, C., & McLachlan, G. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.102102699 · Cited by 1,480
Assessment of PLSDA Cross Validation
Westerhuis, J. A., Hoefsloot, H. C. J., Smit, S., Vis, D. J., Smilde, A. K., van Velzen, E. J. J., van Duijnhoven, J. P. M., & van Dorsten, F. A. (2008) · Metabolomics · doi:10.1007/s11306-007-0099-6 · Cited by 1,471
Multivariate Analysis in Metabolomics
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Cited by
Shahar, O., Botvinnik, A., Shwartz, A., Lerer, E., Golding, P., Buko, A., Hamid, E., Kahn, D., Guralnick, M., Blakolmer, K., et al. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02477-w · Cited by 40
Gubert, C., Love, C. J., Kodikara, S., Mei Liew, J. J., Renoir, T., Lê Cao, K.-A., & Hannan, A. J. (2022) · iScience · doi:10.1016/j.isci.2021.103687 · Cited by 41
Breath VOC analysis and machine learning approaches for disease screening: a review
P, H., Rangarajan, M., & Pandya, H. J. (2023) · Journal of Breath Research · doi:10.1088/1752-7163/acb283 · Cited by 37
Antioxidant activity of the main phenolics found in red fruits: An in vitro and in silico study
Gonçalves, A. C., Falcão, A., Alves, G., Silva, L. R., & Flores-Félix, J. D. (2024) · Food Chemistry · doi:10.1016/j.foodchem.2024.139459 · Cited by 28
Metadata
- Authors
- Ruiz-Perez, D., Guan, H., Madhivanan, P., Mathee, K., & Narasimhan, G.
- Venue
- BMC Bioinformatics
- Published
- 2020-12
- Volume
- 21
- Issue
- S1
- Pages
- 2-2
- ISSN
- 1471-2105 (ISSN-L)
- OpenAlex
- W3112561157
- Cited by
- 426
- References
- 27
- FWCI
- 30.40
- Type
- journal article
- Language
- en
- Open access
- gold
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- Ruiz-Perez, D., Guan, H., Madhivanan, P., Mathee, K., & Narasimhan, G. (2020). So you think you can PLS-DA? BMC Bioinformatics. https://doi.org/10.1186/s12859-019-3310-7
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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