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Article: Artificial neural networks and decision tree model analysis of liver cancer proteomes

TitleArtificial neural networks and decision tree model analysis of liver cancer proteomes
Authors
KeywordsANN
Cancer proteome
CART
Classification
Hepatocellular carcinoma
Issue Date2007
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/622790/description
Citation
Biochemical And Biophysical Research Communications, 2007, v. 361 n. 1, p. 68-73 How to Cite?
AbstractHepatocellular 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. © 2007 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/81617
ISSN
2015 Impact Factor: 2.371
2015 SCImago Journal Rankings: 1.152
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorLuk, JMen_HK
dc.contributor.authorLam, BYen_HK
dc.contributor.authorLee, NPYen_HK
dc.contributor.authorHo, DWen_HK
dc.contributor.authorSham, PCen_HK
dc.contributor.authorChen, Len_HK
dc.contributor.authorPeng, Jen_HK
dc.contributor.authorLeng, Xen_HK
dc.contributor.authorDay, PJen_HK
dc.contributor.authorFan, STen_HK
dc.date.accessioned2010-09-06T08:19:56Z-
dc.date.available2010-09-06T08:19:56Z-
dc.date.issued2007en_HK
dc.identifier.citationBiochemical And Biophysical Research Communications, 2007, v. 361 n. 1, p. 68-73en_HK
dc.identifier.issn0006-291Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/81617-
dc.description.abstractHepatocellular 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. © 2007 Elsevier Inc. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/wps/find/journaldescription.cws_home/622790/descriptionen_HK
dc.relation.ispartofBiochemical and Biophysical Research Communicationsen_HK
dc.subjectANNen_HK
dc.subjectCancer proteomeen_HK
dc.subjectCARTen_HK
dc.subjectClassificationen_HK
dc.subjectHepatocellular carcinomaen_HK
dc.titleArtificial neural networks and decision tree model analysis of liver cancer proteomesen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=0006-291X&volume=361&issue=1&spage=68&epage=73&date=2007&atitle=Artificial+neural+networks+and+decision+tree+model+analysis+of+liver+cancer+proteomesen_HK
dc.identifier.emailLuk, JM: jmluk@hkucc.hku.hken_HK
dc.identifier.emailLee, NPY: nikkilee@hku.hken_HK
dc.identifier.emailSham, PC: pcsham@hku.hken_HK
dc.identifier.emailFan, ST: stfan@hku.hken_HK
dc.identifier.authorityLuk, JM=rp00349en_HK
dc.identifier.authorityLee, NPY=rp00263en_HK
dc.identifier.authoritySham, PC=rp00459en_HK
dc.identifier.authorityFan, ST=rp00355en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.bbrc.2007.06.172en_HK
dc.identifier.pmid17644064-
dc.identifier.scopuseid_2-s2.0-34548693705en_HK
dc.identifier.hkuros133126en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-34548693705&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume361en_HK
dc.identifier.issue1en_HK
dc.identifier.spage68en_HK
dc.identifier.epage73en_HK
dc.identifier.isiWOS:000248659000012-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridLuk, JM=7006777791en_HK
dc.identifier.scopusauthoridLam, BY=7102023588en_HK
dc.identifier.scopusauthoridLee, NPY=7402722690en_HK
dc.identifier.scopusauthoridHo, DW=7402971906en_HK
dc.identifier.scopusauthoridSham, PC=34573429300en_HK
dc.identifier.scopusauthoridChen, L=7409441990en_HK
dc.identifier.scopusauthoridPeng, J=7401958598en_HK
dc.identifier.scopusauthoridLeng, X=7102492468en_HK
dc.identifier.scopusauthoridDay, PJ=7202148832en_HK
dc.identifier.scopusauthoridFan, ST=7402678224en_HK
dc.identifier.citeulike2933448-

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