Classification of genomic and proteomic data using support vector machines
Johansson, Peter LU and Ringnér, Markus LU
(2007)
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)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/798994
- author
- Johansson, Peter
LU
and Ringnér, Markus
LU
- organization
- publishing date
- 2007
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Fundamentals of Data Mining in Genomics and Proteomics
- editor
- Berrar, D. P. ; Dubitzky, W. and Granzow, M
- pages
- 16 pages
- publisher
- Springer
- external identifiers
-
- scopus:57349114111
- ISBN
- 978-1-4020-7260-4
- 978-1-4757-7809-0
- 978-0-306-47815-4
- 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
- 2016-04-04 12:01:44
- date last changed
- 2024-02-28 22:24:30
@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}},
booktitle = {{Fundamentals of Data Mining in Genomics and Proteomics}},
editor = {{Berrar, D. P. and Dubitzky, W. and Granzow, M}},
isbn = {{978-1-4020-7260-4}},
language = {{eng}},
pages = {{187--202}},
publisher = {{Springer}},
title = {{Classification of genomic and proteomic data using support vector machines}},
url = {{http://dx.doi.org/10.1007/978-0-387-47509-7_9}},
doi = {{10.1007/978-0-387-47509-7_9}},
year = {{2007}},
}