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Classification of genomic and proteomic data using support vector machines

Johansson, Peter LU and Ringnér, Markus LU (2007) In Fundamentals of Data Mining in Genomics and Proteomics p.187-202
Abstract
Supervised learning methods are used when one wants to construct a classifier. To use such a method, one has to know the correct classification of at least some samples, which are used to train the classifier. Once a classifier has been trained it can be used to predict the class of unknown samples. Supervised learning methods have been used numerous times in genomic applications and we will only provide some examples here. Different subtypes of cancers such as leukemia (Golub et al., 1999) and small round blue cell tumors (Khan et al., 2001) have been predicted based on their gene expression profiles obtained with microarrays. Microarray data has also been used in the construction of classifiers for the prediction of outcome of patients,... (More)
Supervised learning methods are used when one wants to construct a classifier. To use such a method, one has to know the correct classification of at least some samples, which are used to train the classifier. Once a classifier has been trained it can be used to predict the class of unknown samples. Supervised learning methods have been used numerous times in genomic applications and we will only provide some examples here. Different subtypes of cancers such as leukemia (Golub et al., 1999) and small round blue cell tumors (Khan et al., 2001) have been predicted based on their gene expression profiles obtained with microarrays. Microarray data has also been used in the construction of classifiers for the prediction of outcome of patients, such as whether a breast tumor is likely to give rise to a distant metastasis (van’t Veer et al., 2002) or whether a medulloblastoma patient is likely to have a favorable clinical outcome (Pomeroy et al., 2002). Proteomic patterns in serum have been used to identify ovarian cancer (Petricoin et al., 2002a) and prostate cancer (Adam et al., 2002); (Petricoin et al., 2002b). (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Fundamentals of Data Mining in Genomics and Proteomics
editor
Berrar, D. P.; Dubitzky, W. and Granzow, M
pages
187 - 202
publisher
Springer
external identifiers
  • Scopus:57349114111
ISBN
978-0-387-47508-0
DOI
10.1007/978-0-387-47509-7_9
language
English
LU publication?
yes
id
b3a172fc-6cbc-44aa-8fcb-05109432aa8e (old id 798994)
date added to LUP
2007-12-28 14:14:34
date last changed
2017-01-01 08:06:34
@inbook{b3a172fc-6cbc-44aa-8fcb-05109432aa8e,
  abstract     = {Supervised learning methods are used when one wants to construct a classifier. To use such a method, one has to know the correct classification of at least some samples, which are used to train the classifier. Once a classifier has been trained it can be used to predict the class of unknown samples. Supervised learning methods have been used numerous times in genomic applications and we will only provide some examples here. Different subtypes of cancers such as leukemia (Golub et al., 1999) and small round blue cell tumors (Khan et al., 2001) have been predicted based on their gene expression profiles obtained with microarrays. Microarray data has also been used in the construction of classifiers for the prediction of outcome of patients, such as whether a breast tumor is likely to give rise to a distant metastasis (van’t Veer et al., 2002) or whether a medulloblastoma patient is likely to have a favorable clinical outcome (Pomeroy et al., 2002). Proteomic patterns in serum have been used to identify ovarian cancer (Petricoin et al., 2002a) and prostate cancer (Adam et al., 2002); (Petricoin et al., 2002b).},
  author       = {Johansson, Peter and Ringnér, Markus},
  editor       = {Berrar, D. P. and Dubitzky, W. and Granzow, M},
  isbn         = {978-0-387-47508-0},
  language     = {eng},
  pages        = {187--202},
  publisher    = {Springer},
  series       = {Fundamentals of Data Mining in Genomics and Proteomics},
  title        = {Classification of genomic and proteomic data using support vector machines},
  url          = {http://dx.doi.org/10.1007/978-0-387-47509-7_9},
  year         = {2007},
}