TY - GEN
T1 - A parametric joint model of dna-protein binding, gene expression and dna sequence data to detect target genes of a transcription factor
AU - Pan, Wei
AU - Wei, Peng
AU - Khodursky, Arkady
PY - 2008
Y1 - 2008
N2 - This paper concerns with predicting the regulatory targets of a transcription factor (TF). We propose and study a joint model that combines the use of DNA-protein binding, gene expression and DNA sequence data simultaneously; a parametric mixture model is used to realize unsupervised learning, which however can be extended to semi-supervised learning too. We applied the methods to an E coli dataset to identify the target genes of LexA, which, along with applications to simulated data, demonstrated potential gains of jointly modeling multiple types of data over using only one type of data.
AB - This paper concerns with predicting the regulatory targets of a transcription factor (TF). We propose and study a joint model that combines the use of DNA-protein binding, gene expression and DNA sequence data simultaneously; a parametric mixture model is used to realize unsupervised learning, which however can be extended to semi-supervised learning too. We applied the methods to an E coli dataset to identify the target genes of LexA, which, along with applications to simulated data, demonstrated potential gains of jointly modeling multiple types of data over using only one type of data.
UR - http://www.scopus.com/inward/record.url?scp=40549120695&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=40549120695&partnerID=8YFLogxK
M3 - Conference contribution
C2 - 18229708
AN - SCOPUS:40549120695
SN - 9812776087
SN - 9789812776082
T3 - Pacific Symposium on Biocomputing 2008, PSB 2008
SP - 465
EP - 476
BT - Pacific Symposium on Biocomputing 2008, PSB 2008
T2 - 13th Pacific Symposium on Biocomputing, PSB 2008
Y2 - 4 January 2008 through 8 January 2008
ER -