A parametric joint model of dna-protein binding, gene expression and dna sequence data to detect target genes of a transcription factor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationPacific Symposium on Biocomputing 2008, PSB 2008
Pages465-476
Number of pages12
StatePublished - 2008
Event13th Pacific Symposium on Biocomputing, PSB 2008 - Kohala Coast, HI, United States
Duration: Jan 4 2008Jan 8 2008

Publication series

NamePacific Symposium on Biocomputing 2008, PSB 2008

Other

Other13th Pacific Symposium on Biocomputing, PSB 2008
CountryUnited States
CityKohala Coast, HI
Period1/4/081/8/08

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