ArticleHuman genetics2003
Prediction of clinical outcome with microarray data: a partial least squares discriminant analysis (PLS-DA) approach.
Article in Human genetics, 2003. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 143 papers, 1 of them a synthesis that pooled it.
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Who cites it
143 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
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83 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Partial least squares discriminant analysis (PLS-DA) is a partial least squares regression of a set Y of binary variables describing the categories of a categorical variable on a set X of predictor variables. It is a compromise between the usual discriminant analysis and a discriminant analysis on the significant principal components of the predictor variables. This technique is specially suited to deal with a much larger number of predictors than observations and with multicollineality, two of the main problems encountered when analysing microarray expression data. We explore the performance of PLS-DA with published data from breast cancer (Perou et al. 2000). Several such analyses were carried out: (1) before vs after chemotherapy treatment, (2) estrogen receptor positive vs negative tumours, and (3) tumour classification. We found that the performance of PLS-DA was extremely satisfactory in all cases and that the discriminant cDNA clones often had a sound biological interpretation. We conclude that PLS-DA is a powerful yet simple tool for analysing microarray data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.