Abstract
This paper proposes a novel algorithm for inferring genetic regulatory networks by exploiting the knowledge of gene expressions, DNA sequences and binding sites. The integration of multiple data sources helps to improve both the specificity and the sensitivity of network inference. The transcription factors of a target gene are determined by applying the reversible jump Markov chain Monte-Carlo (RJMCMC) algorithm to the linear regression model. The scheme is simulated on yeast data and the results provide insight on the regulation mechanism associated with environmental changes.
Original language | English |
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Title of host publication | 5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07 |
DOIs | |
Publication status | Published - 2007 |
Externally published | Yes |
Event | 5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07 - Tuusula, Finland Duration: 10 Jun 2007 → 12 Jun 2007 |
Other
Other | 5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07 |
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Country | Finland |
City | Tuusula |
Period | 10/6/07 → 12/6/07 |
Fingerprint
ASJC Scopus subject areas
- Biochemistry, Genetics and Molecular Biology(all)
- Computer Science Applications
- Signal Processing
- Electrical and Electronic Engineering
Cite this
Recovering genetic regulatory networks by integrating multiple data sources. / Zhao, Wentao; Serpedin, Erchin; Dougherty, Edward R.
5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07. 2007. 4365813.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Recovering genetic regulatory networks by integrating multiple data sources
AU - Zhao, Wentao
AU - Serpedin, Erchin
AU - Dougherty, Edward R.
PY - 2007
Y1 - 2007
N2 - This paper proposes a novel algorithm for inferring genetic regulatory networks by exploiting the knowledge of gene expressions, DNA sequences and binding sites. The integration of multiple data sources helps to improve both the specificity and the sensitivity of network inference. The transcription factors of a target gene are determined by applying the reversible jump Markov chain Monte-Carlo (RJMCMC) algorithm to the linear regression model. The scheme is simulated on yeast data and the results provide insight on the regulation mechanism associated with environmental changes.
AB - This paper proposes a novel algorithm for inferring genetic regulatory networks by exploiting the knowledge of gene expressions, DNA sequences and binding sites. The integration of multiple data sources helps to improve both the specificity and the sensitivity of network inference. The transcription factors of a target gene are determined by applying the reversible jump Markov chain Monte-Carlo (RJMCMC) algorithm to the linear regression model. The scheme is simulated on yeast data and the results provide insight on the regulation mechanism associated with environmental changes.
UR - http://www.scopus.com/inward/record.url?scp=47049129670&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=47049129670&partnerID=8YFLogxK
U2 - 10.1109/GENSIPS.2007.4365813
DO - 10.1109/GENSIPS.2007.4365813
M3 - Conference contribution
AN - SCOPUS:47049129670
SN - 1424409993
SN - 9781424409990
BT - 5th IEEE International Workshop on Genomic Signal Processing and Statistics, GENSIPS'07
ER -