A hidden markov model approach for prediction of genomic alterations from gene expression profiling

Huimin Geng; Hesham H. Ali; Wing C. Chan

doi:10.1007/978-3-540-79450-9_38

A hidden markov model approach for prediction of genomic alterations from gene expression profiling

Huimin Geng, Hesham H. Ali, Wing C. Chan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Scopus citations

Abstract

The mRNA transcript changes detected by Gene Expression Profiling (GEP) have been found to be correlated with corresponding DNA copy number variations detected by Comparative Genomic Hybridization (CGH). This correlation, together with the availability of genome-wide, high-density GEP arrays, supports that it is possible to predict genomic alterations from GEP data in tumors. In this paper, we proposed a hidden Markov model-based CGH predictor, HMM_CGH, which was trained in the light of the paired experimental GEP and CGH data on a sufficient number of cases, and then applied to new cases for the prediction of chromosomal gains and losses from their GEP data. The HMM_CGH predictor, taking advantage of the rich GEP data already available to derive genomic alterations, could enhance the detection of genetic abnormalities in tumors. The results from the analysis of lymphoid malignancies validated the model with 80% sensitivity, 90% specificity and 90% accuracy in predicting both gains and losses.

Original language	English (US)
Title of host publication	Bioinformatics Research and Applications - Fourth International Symposium, ISBRA 2008, Proceedings
Pages	414-425
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-540-79450-9_38
State	Published - Aug 27 2008
Event	4th International Symposium on Bioinformatics Research and Applications, ISBRA 2008 - Atlanta, GA, United States Duration: May 6 2008 → May 9 2008

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4983 LNBI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	4th International Symposium on Bioinformatics Research and Applications, ISBRA 2008
Country	United States
City	Atlanta, GA
Period	5/6/08 → 5/9/08

Keywords

Comparative Genomic Hybridization (CGH)
Gene Expression Profiling (GEP)
Genomic alterations
Hidden Markov Model (HMM)

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-540-79450-9_38

Fingerprint Dive into the research topics of 'A hidden markov model approach for prediction of genomic alterations from gene expression profiling'. Together they form a unique fingerprint.

Cite this

Geng, H., Ali, H. H., & Chan, W. C. (2008). A hidden markov model approach for prediction of genomic alterations from gene expression profiling. In Bioinformatics Research and Applications - Fourth International Symposium, ISBRA 2008, Proceedings (pp. 414-425). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4983 LNBI). https://doi.org/10.1007/978-3-540-79450-9_38

A hidden markov model approach for prediction of genomic alterations from gene expression profiling. / Geng, Huimin; Ali, Hesham H.; Chan, Wing C.

Bioinformatics Research and Applications - Fourth International Symposium, ISBRA 2008, Proceedings. 2008. p. 414-425 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4983 LNBI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Geng, H, Ali, HH & Chan, WC 2008, A hidden markov model approach for prediction of genomic alterations from gene expression profiling. in Bioinformatics Research and Applications - Fourth International Symposium, ISBRA 2008, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4983 LNBI, pp. 414-425, 4th International Symposium on Bioinformatics Research and Applications, ISBRA 2008, Atlanta, GA, United States, 5/6/08. https://doi.org/10.1007/978-3-540-79450-9_38

Geng H, Ali HH, Chan WC. A hidden markov model approach for prediction of genomic alterations from gene expression profiling. In Bioinformatics Research and Applications - Fourth International Symposium, ISBRA 2008, Proceedings. 2008. p. 414-425. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-79450-9_38

Geng, Huimin ; Ali, Hesham H. ; Chan, Wing C. / A hidden markov model approach for prediction of genomic alterations from gene expression profiling. Bioinformatics Research and Applications - Fourth International Symposium, ISBRA 2008, Proceedings. 2008. pp. 414-425 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{3a3daf331dc247a2b08dac43e3c61113,

title = "A hidden markov model approach for prediction of genomic alterations from gene expression profiling",

abstract = "The mRNA transcript changes detected by Gene Expression Profiling (GEP) have been found to be correlated with corresponding DNA copy number variations detected by Comparative Genomic Hybridization (CGH). This correlation, together with the availability of genome-wide, high-density GEP arrays, supports that it is possible to predict genomic alterations from GEP data in tumors. In this paper, we proposed a hidden Markov model-based CGH predictor, HMM_CGH, which was trained in the light of the paired experimental GEP and CGH data on a sufficient number of cases, and then applied to new cases for the prediction of chromosomal gains and losses from their GEP data. The HMM_CGH predictor, taking advantage of the rich GEP data already available to derive genomic alterations, could enhance the detection of genetic abnormalities in tumors. The results from the analysis of lymphoid malignancies validated the model with 80% sensitivity, 90% specificity and 90% accuracy in predicting both gains and losses.",

keywords = "Comparative Genomic Hybridization (CGH), Gene Expression Profiling (GEP), Genomic alterations, Hidden Markov Model (HMM)",

author = "Huimin Geng and Ali, {Hesham H.} and Chan, {Wing C.}",

year = "2008",

month = aug,

day = "27",

doi = "10.1007/978-3-540-79450-9_38",

language = "English (US)",

isbn = "3540794492",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

pages = "414--425",

booktitle = "Bioinformatics Research and Applications - Fourth International Symposium, ISBRA 2008, Proceedings",

note = "4th International Symposium on Bioinformatics Research and Applications, ISBRA 2008 ; Conference date: 06-05-2008 Through 09-05-2008",

}

TY - GEN

T1 - A hidden markov model approach for prediction of genomic alterations from gene expression profiling

AU - Geng, Huimin

AU - Ali, Hesham H.

AU - Chan, Wing C.

PY - 2008/8/27

Y1 - 2008/8/27

N2 - The mRNA transcript changes detected by Gene Expression Profiling (GEP) have been found to be correlated with corresponding DNA copy number variations detected by Comparative Genomic Hybridization (CGH). This correlation, together with the availability of genome-wide, high-density GEP arrays, supports that it is possible to predict genomic alterations from GEP data in tumors. In this paper, we proposed a hidden Markov model-based CGH predictor, HMM_CGH, which was trained in the light of the paired experimental GEP and CGH data on a sufficient number of cases, and then applied to new cases for the prediction of chromosomal gains and losses from their GEP data. The HMM_CGH predictor, taking advantage of the rich GEP data already available to derive genomic alterations, could enhance the detection of genetic abnormalities in tumors. The results from the analysis of lymphoid malignancies validated the model with 80% sensitivity, 90% specificity and 90% accuracy in predicting both gains and losses.

AB - The mRNA transcript changes detected by Gene Expression Profiling (GEP) have been found to be correlated with corresponding DNA copy number variations detected by Comparative Genomic Hybridization (CGH). This correlation, together with the availability of genome-wide, high-density GEP arrays, supports that it is possible to predict genomic alterations from GEP data in tumors. In this paper, we proposed a hidden Markov model-based CGH predictor, HMM_CGH, which was trained in the light of the paired experimental GEP and CGH data on a sufficient number of cases, and then applied to new cases for the prediction of chromosomal gains and losses from their GEP data. The HMM_CGH predictor, taking advantage of the rich GEP data already available to derive genomic alterations, could enhance the detection of genetic abnormalities in tumors. The results from the analysis of lymphoid malignancies validated the model with 80% sensitivity, 90% specificity and 90% accuracy in predicting both gains and losses.

KW - Comparative Genomic Hybridization (CGH)

KW - Gene Expression Profiling (GEP)

KW - Genomic alterations

KW - Hidden Markov Model (HMM)

UR - http://www.scopus.com/inward/record.url?scp=49949100280&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=49949100280&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-79450-9_38

DO - 10.1007/978-3-540-79450-9_38

M3 - Conference contribution

AN - SCOPUS:49949100280

SN - 3540794492

SN - 9783540794493

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 414

EP - 425

BT - Bioinformatics Research and Applications - Fourth International Symposium, ISBRA 2008, Proceedings

T2 - 4th International Symposium on Bioinformatics Research and Applications, ISBRA 2008

Y2 - 6 May 2008 through 9 May 2008

ER -