IR@PKUHSC  > 北京大学第二临床医学院
学科主题临床医学
Artificial neural networks and decision tree model analysis of liver cancer proteomes
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
2007-09-14
DOI10.1016/j.bbrc.2007.06.172
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
引用统计
被引频次:27[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.bjmu.edu.cn/handle/400002259/55622
专题北京大学第二临床医学院
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
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GB/T 7714
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.
APA Luk, John M..,Lam, Brian Y..,Lee, Nikki P. Y..,Ho, David W..,Sham, Pak C..,...&Fan, Sheung-Tat.(2007).Artificial neural networks and decision tree model analysis of liver cancer proteomes.BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS,361(1),68-73.
MLA Luk, John M.,et al."Artificial neural networks and decision tree model analysis of liver cancer proteomes".BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 361.1(2007):68-73.
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