北京大学医学部机构知识库
Advanced  
IR@PKUHSC  > 北京大学第二临床医学院  > 期刊论文
学科主题: 临床医学
题名:
Artificial neural networks and decision tree model analysis of liver cancer proteomes
作者: Luk, John M.; Lam, Brian Y.; Lee, Nikki P. Y.; Ho, David W.; Sham, Pak C.; Chen, Lei; Peng, Jirun; Leng, Xisheng; Day, Philip J.; Fan, Sheung-Tat
关键词: cancer proteome ; classification ; CART ; ANN ; hepatocellular carcinoma
刊名: BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS
发表日期: 2007-09-14
DOI: 10.1016/j.bbrc.2007.06.172
卷: 361, 期:1, 页:68-73
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology
类目[WOS]: Biochemistry & Molecular Biology ; Biophysics
研究领域[WOS]: Biochemistry & Molecular Biology ; Biophysics
关键词[WOS]: HEPATOCELLULAR-CARCINOMA ; HEPATITIS-C ; IDENTIFICATION ; PREDICTION ; DISCOVERY ; PROTEINS ; EXPOSURE ; MARKERS ; TUMOR
英文摘要:

Hepatocellular carcinoma (HCC) is a heterogeneous cancer and usually diagnosed at late advanced tumor stages of high lethality. The present study attempted to obtain a proteome-wide analysis of HCC in comparison with adjacent non-tumor liver tissues, in order to facilitate biomarkers′ discovery and to investigate the mechanisms of HCC development. A cohort of 66 Chinese patients with HCC was included for proteomic profiling study by two-dimensional gel electrophoresis (2-DE) analysis. Artificial neural network (ANN) and decision tree (CART) data-mining methods were employed to analyze the profiling data and to delineate significant patterns and trends for discriminating HCC from non-malignant liver tissues. Protein markers were identified by tandem MS/MS. A total of 132 proteome datasets were generated by 2-DE expression profiling analysis, and each with 230 consolidated protein expression intensities. Both the data-mining algorithms successfully distinguished the HCC phenotype from other non-malignant liver samples. The detection sensitivity and specificity of ANN were 96.97% and 87.88%, while those of CART were 81.82% and 78.79%, respectively. The three biological classifiers in the CART model were identified as cytochrome b5, heat shock 70 kDa protein 8 isoform 2, and cathepsin B. The 2-DE-based proteomic profiling approach combined with the ANN or CART algorithm yielded satisfactory performance on identifying HCC and revealed potential candidate cancer biomarkers. (c) 2007 Elsevier Inc. All rights reserved.

语种: 英语
WOS记录号: WOS:000248659000012
Citation statistics:
内容类型: 期刊论文
URI标识: http://ir.bjmu.edu.cn/handle/400002259/55622
Appears in Collections:北京大学第二临床医学院_期刊论文

Files in This Item:

There are no files associated with this item.


作者单位: 1.Univ Hong Kong, Fac Med, Dept Surg, Hong Kong, Hong Kong, Peoples R China
2.Univ Hong Kong, Fac Med, Ctr Canc Res, Hong Kong, Hong Kong, Peoples R China
3.Univ Hong Kong, Genome Res Ctr, Hong Kong, Hong Kong, Peoples R China
4.Univ Hong Kong, Dept Psychiat, Hong Kong, Hong Kong, Peoples R China
5.Peking Univ, Peoples Hosp, Dept Surg, Beijing 100871, Peoples R China
6.Univ Manchester, Manchester Interdisciplinary Bioctr, Manchester, Lancs, England

Recommended Citation:
Luk, John M.,Lam, Brian Y.,Lee, Nikki P. Y.,et al. Artificial neural networks and decision tree model analysis of liver cancer proteomes[J]. BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS,2007,361(1):68-73.
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Luk, John M.]'s Articles
[Lam, Brian Y.]'s Articles
[Lee, Nikki P. Y.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Luk, John M.]‘s Articles
[Lam, Brian Y.]‘s Articles
[Lee, Nikki P. Y.]‘s Articles
Related Copyright Policies
Null
Social Bookmarking
Add to CiteULike Add to Connotea Add to Del.icio.us Add to Digg Add to Reddit

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.

 

 

Valid XHTML 1.0!
Copyright © 2007-2017  北京大学医学部 - Feedback
Powered by CSpace