A combined expression-interaction model for inferring the temporal activity of transcription factors

Authors:
Yanxin Shi;Itamar Simon;Tom Mitchell;Ziv Bar-Joseph
Affiliations:
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;Dept of Molecular Biology, Hebrew University Medical School, Jerusalem, Israel;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
Venue:
RECOMB'08 Proceedings of the 12th annual international conference on Research in computational molecular biology
Year:
2008

Citing 11
Cited 1

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Factorial Hidden Markov Models

Machine Learning - Special issue on learning with probabilistic representations
Dynamic bayesian networks: representation, inference and learning

Dynamic bayesian networks: representation, inference and learning
Inferring quantitative models of regulatory networks from expression data

Bioinformatics
A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data

Bioinformatics
Hidden process models

ICML '06 Proceedings of the 23rd international conference on Machine learning
Bayesian sparse hidden components analysis for transcription regulation networks

Bioinformatics
Group SCAD regression analysis for microarray time course gene expression data

Bioinformatics
Continuous hidden process model for time series expression experiments

Bioinformatics
A Bayesian model that links microarray mRNA measurements to mass spectrometry protein measurements

RECOMB'07 Proceedings of the 11th annual international conference on Research in computational molecular biology
A generalized mean field algorithm for variational inference in exponential families

UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence

Modelling transcriptional regulation with a mixture of factor analyzers and variational Bayesian expectation maximization

EURASIP Journal on Bioinformatics and Systems Biology

Quantified Score

Hi-index	0.02

Visualization

Abstract

Methods suggested for reconstructing regulatory networks can be divided into two sets based on how the activity level of transcription factors (TFs) is inferred. The first group of methods relies on the expression levels of TFs assuming that the activity of a TF is highly correlated with its mRNA abundance. The second treats the activity level as unobserved and infers it from the expression of the genes the TF regulates. While both types of methods were successfully applied, each suffers from drawbacks that limit their accuracy. For the first set, the assumption that mRNA levels are correlated with activity is violated for many TFs due to post-transcriptional modifications. For the second, the expression level of a TF which might be informative is completely ignored. Here we present the Post-Transcriptional Modification Model (PTMM) that unlike previous methods utilizes both sources of data concurrently. Our method uses a switching model to determine whether a TF is transcriptionally or post-transcriptionally regulated. This model is combined with a factorial HMM to fully reconstruct the interactions in a dynamic regulatory network. Using simulated and real data we show that PTMM outperforms the other two approaches discussed above. Using real data we also show that PTMM can recover meaningful TF activity levels and identify post-transcriptionally modified TFs, many of which are supported by other sources.