schliessen

Filtern

 

Bibliotheken

A Classification Method Based on Principal Components of SELDI Spectra to Diagnose of Lung Adenocarcinoma (Classification Model Based on Principal Components)

Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on princi... Full description

Journal Title: PLoS ONE 2012, Vol.7(3), p.e34457
Main Author: Lin, Qiang
Other Authors: Peng, Qianqian , Yao, Feng , Pan, Xu-Feng , Xiong, Li-Wen , Wang, Yi , Geng, Jun-Feng , Feng, Jiu-Xian , Han, Bao-Hui , Bao, Guo-Liang , Yang, Yu , Wang, Xiaotian , Jin, Li , Guo, Wensheng , Wang, Jiu-Cun
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0034457
Zum Text:
SendSend as email Add to Book BagAdd to Book Bag
Staff View
recordid: plos10.1371/journal.pone.0034457
title: A Classification Method Based on Principal Components of SELDI Spectra to Diagnose of Lung Adenocarcinoma (Classification Model Based on Principal Components)
format: Article
creator:
  • Lin, Qiang
  • Peng, Qianqian
  • Yao, Feng
  • Pan, Xu-Feng
  • Xiong, Li-Wen
  • Wang, Yi
  • Geng, Jun-Feng
  • Feng, Jiu-Xian
  • Han, Bao-Hui
  • Bao, Guo-Liang
  • Yang, Yu
  • Wang, Xiaotian
  • Jin, Li
  • Guo, Wensheng
  • Wang, Jiu-Cun
subjects:
  • Research Article
  • Biology
  • Mathematics
  • Medicine
  • Genetics And Genomics
  • Oncology
  • Biochemistry
  • Mathematics
ispartof: PLoS ONE, 2012, Vol.7(3), p.e34457
description: Lung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods. ; The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis. ; The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use.
language: eng
source:
identifier: E-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0034457
fulltext: fulltext
issn:
  • 1932-6203
  • 19326203
url: Link


@attributes
ID110912262
RANK0.07
NO1
SEARCH_ENGINEprimo_central_multiple_fe
SEARCH_ENGINE_TYPEPrimo Central Search Engine
LOCALfalse
PrimoNMBib
record
control
sourcerecordid10.1371/journal.pone.0034457
sourceidplos
recordidTN_plos10.1371/journal.pone.0034457
sourcesystemPC
pqid963488270
galeid477040124
display
typearticle
titleA Classification Method Based on Principal Components of SELDI Spectra to Diagnose of Lung Adenocarcinoma (Classification Model Based on Principal Components)
creatorLin, Qiang ; Peng, Qianqian ; Yao, Feng ; Pan, Xu-Feng ; Xiong, Li-Wen ; Wang, Yi ; Geng, Jun-Feng ; Feng, Jiu-Xian ; Han, Bao-Hui ; Bao, Guo-Liang ; Yang, Yu ; Wang, Xiaotian ; Jin, Li ; Guo, Wensheng ; Wang, Jiu-Cun
contributorCho, William C. S. (Editor)
ispartofPLoS ONE, 2012, Vol.7(3), p.e34457
identifierE-ISSN: 1932-6203 ; DOI: 10.1371/journal.pone.0034457
subjectResearch Article ; Biology ; Mathematics ; Medicine ; Genetics And Genomics ; Oncology ; Biochemistry ; Mathematics
descriptionLung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods. ; The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis. ; The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use.
languageeng
source
version9
lds50peer_reviewed
links
openurl$$Topenurl_article
openurlfulltext$$Topenurlfull_article
search
creatorcontrib
0Lin, Qiang
1Peng, Qianqian
2Yao, Feng
3Pan, Xu-Feng
4Xiong, Li-Wen
5Wang, Yi
6Geng, Jun-Feng
7Feng, Jiu-Xian
8Han, Bao-Hui
9Bao, Guo-Liang
10Yang, Yu
11Wang, Xiaotian
12Jin, Li
13Guo, Wensheng
14Wang, Jiu-Cun
15Cho, William C. S. (Editor)
titleA Classification Method Based on Principal Components of SELDI Spectra to Diagnose of Lung Adenocarcinoma (Classification Model Based on Principal Components)
descriptionLung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods. ; The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis. ; The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use.
subject
0Research Article
1Biology
2Mathematics
3Medicine
4Genetics And Genomics
5Oncology
6Biochemistry
general
010.1371/journal.pone.0034457
1English
sourceidplos
recordidplos10.1371/journal.pone.0034457
issn
01932-6203
119326203
rsrctypearticle
creationdate2012
recordtypearticle
addtitle
0PLoS ONE
1Classification Model Based on Principal Components
searchscopeplos
scopeplos
lsr30VSR-Enriched:[pqid, galeid]
sort
titleA Classification Method Based on Principal Components of SELDI Spectra to Diagnose of Lung Adenocarcinoma (Classification Model Based on Principal Components)
authorLin, Qiang ; Peng, Qianqian ; Yao, Feng ; Pan, Xu-Feng ; Xiong, Li-Wen ; Wang, Yi ; Geng, Jun-Feng ; Feng, Jiu-Xian ; Han, Bao-Hui ; Bao, Guo-Liang ; Yang, Yu ; Wang, Xiaotian ; Jin, Li ; Guo, Wensheng ; Wang, Jiu-Cun
creationdate20120326
facets
frbrgroupid5987497527675748364
frbrtype5
languageeng
creationdate2012
topic
0Research Article
1Biology
2Mathematics
3Medicine
4Genetics And Genomics
5Oncology
6Biochemistry
collectionPLoS
prefilterarticles
rsrctypearticles
creatorcontrib
0Lin, Qiang
1Peng, Qianqian
2Yao, Feng
3Pan, Xu-Feng
4Xiong, Li-Wen
5Wang, Yi
6Geng, Jun-Feng
7Feng, Jiu-Xian
8Han, Bao-Hui
9Bao, Guo-Liang
10Yang, Yu
11Wang, Xiaotian
12Jin, Li
13Guo, Wensheng
14Wang, Jiu-Cun
15Cho, William C. S.
jtitlePLoS ONE
toplevelpeer_reviewed
frbr
t2
k12012
k219326203
k310.1371/journal.pone.0034457
k47
k53
k634457
k7plos one
k8classification method based on principal components of seldi spectra to diagnose of lung adenocarcinoma
k9classificationmethodinoma
k12classificationmethodbased
k15qianglin
k16linqiang
delivery
delcategoryRemote Search Resource
fulltextfulltext
ranking
booster11
booster21
pcg_typepublisher
addata
aulast
0Lin
1Peng
2Yao
3Pan
4Xiong
5Wang
6Geng
7Feng
8Han
9Bao
10Yang
11Jin
12Guo
13Cho
aufirst
0Qiang
1Qianqian
2Feng
3Xu-Feng
4Li-Wen
5Yi
6Jun-Feng
7Jiu-Xian
8Bao-Hui
9Guo-Liang
10Yu
11Xiaotian
12Li
13Wensheng
14Jiu-Cun
15William C. S.
au
0Lin, Qiang
1Peng, Qianqian
2Yao, Feng
3Pan, Xu-Feng
4Xiong, Li-Wen
5Wang, Yi
6Geng, Jun-Feng
7Feng, Jiu-Xian
8Han, Bao-Hui
9Bao, Guo-Liang
10Yang, Yu
11Wang, Xiaotian
12Jin, Li
13Guo, Wensheng
14Wang, Jiu-Cun
addauCho, William C. S.
atitleA Classification Method Based on Principal Components of SELDI Spectra to Diagnose of Lung Adenocarcinoma (Classification Model Based on Principal Components)
jtitlePLoS ONE
risdate20120326
volume7
issue3
spagee34457
pagese34457
eissn1932-6203
genrearticle
ristypeJOUR
abstractLung cancer is the leading cause of cancer death worldwide, but techniques for effective early diagnosis are still lacking. Proteomics technology has been applied extensively to the study of the proteins involved in carcinogenesis. In this paper, a classification method was developed based on principal components of surface-enhanced laser desorption/ionization (SELDI) spectral data. This method was applied to SELDI spectral data from 71 lung adenocarcinoma patients and 24 healthy individuals. Unlike other peak-selection-based methods, this method takes each spectrum as a unity. The aim of this paper was to demonstrate that this unity-based classification method is more robust and powerful as a method of diagnosis than peak-selection-based methods. ; The results showed that this classification method, which is based on principal components, has outstanding performance with respect to distinguishing lung adenocarcinoma patients from normal individuals. Through leaving-one-out, 19-fold, 5-fold and 2-fold cross-validation studies, we found that this classification method based on principal components completely outperforms peak-selection-based methods, such as decision tree, classification and regression tree, support vector machine, and linear discriminant analysis. ; The classification method based on principal components of SELDI spectral data is a robust and powerful means of diagnosing lung adenocarcinoma. We assert that the high efficiency of this classification method renders it feasible for large-scale clinical use.
copSan Francisco, USA
pubPublic Library of Science
doi10.1371/journal.pone.0034457
oafree_for_read
date2012-03-26