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
Y1 - 2008
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)
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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 -