Cross-platform analysis of cancer biomarkers: A Bayesian network approach to incorporating mass spectrometry and microarray data

Xutao Deng, Huimin Geng, Hesham H Ali

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

Many studies showed inconsistent cancer biomarkers due to bioinformatics artifacts. In this paper we use multiple data sets from microarrays, mass spectrometry, protein sequences, and other biological knowledge in order to improve the reliability of cancer biomarkers. We present a novel Bayesian network (BN) model which integrates and cross-annotates multiple data sets related to prostate cancer. The main contribution of this study is that we provide a method that is designed to find cancer biomarkers whose presence is supported by multiple data sources and biological knowledge. Relevant biological knowledge is explicitly encoded into the model parameters, and the biomarker finding problem is formulated as a Bayesian inference problem. Besides diagnostic accuracy, we introduce reliability as another quality measurement of the biological relevance of biomarkers. Based on the proposed BN model, we develop an empirical scoring scheme and a simulation algorithm for inferring biomarkers. Fourteen genes/proteins including prostate specific antigen (PSA) are identified as reliable serum biomarkers which are insensitive to the model assumptions. The computational results show that our method is able to find biologically relevant biomarkers with highest reliability while maintaining competitive predictive power. In addition, by combining biological knowledge and data from multiple platforms, the number of putative biomarkers is greatly reduced to allow more-focused clinical studies.

Original languageEnglish (US)
Pages (from-to)183-202
Number of pages20
JournalCancer Informatics
Volume3
StatePublished - Dec 1 2007

Fingerprint

Bayes Theorem
Tumor Biomarkers
Mass Spectrometry
Biomarkers
Information Storage and Retrieval
Prostate-Specific Antigen
Computational Biology
Artifacts
Prostatic Neoplasms
Proteins
Serum

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

Cite this

Cross-platform analysis of cancer biomarkers : A Bayesian network approach to incorporating mass spectrometry and microarray data. / Deng, Xutao; Geng, Huimin; Ali, Hesham H.

In: Cancer Informatics, Vol. 3, 01.12.2007, p. 183-202.

Research output: Contribution to journalArticle

@article{c5b1f2cea8e44baabecf4312c1e4bf5d,
title = "Cross-platform analysis of cancer biomarkers: A Bayesian network approach to incorporating mass spectrometry and microarray data",
abstract = "Many studies showed inconsistent cancer biomarkers due to bioinformatics artifacts. In this paper we use multiple data sets from microarrays, mass spectrometry, protein sequences, and other biological knowledge in order to improve the reliability of cancer biomarkers. We present a novel Bayesian network (BN) model which integrates and cross-annotates multiple data sets related to prostate cancer. The main contribution of this study is that we provide a method that is designed to find cancer biomarkers whose presence is supported by multiple data sources and biological knowledge. Relevant biological knowledge is explicitly encoded into the model parameters, and the biomarker finding problem is formulated as a Bayesian inference problem. Besides diagnostic accuracy, we introduce reliability as another quality measurement of the biological relevance of biomarkers. Based on the proposed BN model, we develop an empirical scoring scheme and a simulation algorithm for inferring biomarkers. Fourteen genes/proteins including prostate specific antigen (PSA) are identified as reliable serum biomarkers which are insensitive to the model assumptions. The computational results show that our method is able to find biologically relevant biomarkers with highest reliability while maintaining competitive predictive power. In addition, by combining biological knowledge and data from multiple platforms, the number of putative biomarkers is greatly reduced to allow more-focused clinical studies.",
author = "Xutao Deng and Huimin Geng and Ali, {Hesham H}",
year = "2007",
month = "12",
day = "1",
language = "English (US)",
volume = "3",
pages = "183--202",
journal = "Cancer Informatics",
issn = "1176-9351",
publisher = "Libertas Academica Ltd.",

}

TY - JOUR

T1 - Cross-platform analysis of cancer biomarkers

T2 - A Bayesian network approach to incorporating mass spectrometry and microarray data

AU - Deng, Xutao

AU - Geng, Huimin

AU - Ali, Hesham H

PY - 2007/12/1

Y1 - 2007/12/1

N2 - Many studies showed inconsistent cancer biomarkers due to bioinformatics artifacts. In this paper we use multiple data sets from microarrays, mass spectrometry, protein sequences, and other biological knowledge in order to improve the reliability of cancer biomarkers. We present a novel Bayesian network (BN) model which integrates and cross-annotates multiple data sets related to prostate cancer. The main contribution of this study is that we provide a method that is designed to find cancer biomarkers whose presence is supported by multiple data sources and biological knowledge. Relevant biological knowledge is explicitly encoded into the model parameters, and the biomarker finding problem is formulated as a Bayesian inference problem. Besides diagnostic accuracy, we introduce reliability as another quality measurement of the biological relevance of biomarkers. Based on the proposed BN model, we develop an empirical scoring scheme and a simulation algorithm for inferring biomarkers. Fourteen genes/proteins including prostate specific antigen (PSA) are identified as reliable serum biomarkers which are insensitive to the model assumptions. The computational results show that our method is able to find biologically relevant biomarkers with highest reliability while maintaining competitive predictive power. In addition, by combining biological knowledge and data from multiple platforms, the number of putative biomarkers is greatly reduced to allow more-focused clinical studies.

AB - Many studies showed inconsistent cancer biomarkers due to bioinformatics artifacts. In this paper we use multiple data sets from microarrays, mass spectrometry, protein sequences, and other biological knowledge in order to improve the reliability of cancer biomarkers. We present a novel Bayesian network (BN) model which integrates and cross-annotates multiple data sets related to prostate cancer. The main contribution of this study is that we provide a method that is designed to find cancer biomarkers whose presence is supported by multiple data sources and biological knowledge. Relevant biological knowledge is explicitly encoded into the model parameters, and the biomarker finding problem is formulated as a Bayesian inference problem. Besides diagnostic accuracy, we introduce reliability as another quality measurement of the biological relevance of biomarkers. Based on the proposed BN model, we develop an empirical scoring scheme and a simulation algorithm for inferring biomarkers. Fourteen genes/proteins including prostate specific antigen (PSA) are identified as reliable serum biomarkers which are insensitive to the model assumptions. The computational results show that our method is able to find biologically relevant biomarkers with highest reliability while maintaining competitive predictive power. In addition, by combining biological knowledge and data from multiple platforms, the number of putative biomarkers is greatly reduced to allow more-focused clinical studies.

UR - http://www.scopus.com/inward/record.url?scp=43049127535&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=43049127535&partnerID=8YFLogxK

M3 - Article

C2 - 19455243

AN - SCOPUS:43049127535

VL - 3

SP - 183

EP - 202

JO - Cancer Informatics

JF - Cancer Informatics

SN - 1176-9351

ER -