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Diagnosis of multiple cancer types by shrunken centroids of gene expression
Open accessAbstract
We have devised an approach to cancer class prediction from gene expression profiling, based on an enhancement of the simple nearest prototype (centroid) classifier. We shrink the prototypes and hence obtain a classifier that is often more accurate than competing methods. Our method of “nearest shrunken centroids” identifies subsets of genes that best characterize each class. The technique is general and can be used in many other classification problems. To demonstrate its effectiveness, we show that the method was highly efficient in finding genes for classifying small round blue cell tumors and leukemias.
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“1) Excel add-in to identify genes that best characterize each of two given classes (IFN induced depression or no depression) using the nearest shrunken centroid method [37].”
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Metadata
- Authors
- Tibshirani, R., Hastie, T., Narasimhan, B., & Chu, G.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2002-05-14
- Volume
- 99
- Issue
- 10
- Pages
- 6567-6572
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2138550913
- Cited by
- 2,930
- References
- 4
- FWCI
- 39.53
- Type
- journal article
- Language
- en
- Open access
- green
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