Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities

Pei GaoAntti HonkelaMagnus RattrayNeil D. Lawrence
,  24:0-0, 2008.

Abstract

**Motivation:** Inference of *latent chemical species* in biochemical interaction networks is a key problem in estimation of the structure and parameters of the genetic, metabolic and protein interaction networks that underpin all biological processes. We present a framework for Bayesian marginalisation of these latent chemical species through Gaussian process priors.\ \ **Results:** We demonstrate our general approach on three different biological examples of single input motifs, including both activation and repression of transcription. We focus in particular on the problem of inferring transcription factor activity when the concentration of active protein cannot easily be measured. We show how the uncertainty in the inferred transcription factor activity can be integrated out in order to derive a likelihood function that can be used for the estimation of regulatory model parameters. An advantage of our approach is that we avoid the use of a coarse-grained discretization of continuous-time functions, which would lead to a large number of additional parameters to be estimated. We develop efficient exact and approximate inference schemes, which are much more efficient than competing sampling-based schemes and therefore provide us with a practical toolkit for model-based inference.\ \ **Availability:** The software and data for recreating all the experiments in this paper is available in MATLAB from \ \ **Contact:** Neil Lawrence

Cite this Paper


BibTeX
@InProceedings{pmlr-v-gao-latent08, title = {Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities}, author = {Pei Gao and Antti Honkela and Magnus Rattray and Neil D. Lawrence}, pages = {0--0}, year = {}, editor = {}, volume = {24}, url = {http://inverseprobability.com/publications/gao-latent08.html}, abstract = {**Motivation:** Inference of *latent chemical species* in biochemical interaction networks is a key problem in estimation of the structure and parameters of the genetic, metabolic and protein interaction networks that underpin all biological processes. We present a framework for Bayesian marginalisation of these latent chemical species through Gaussian process priors.\ \ **Results:** We demonstrate our general approach on three different biological examples of single input motifs, including both activation and repression of transcription. We focus in particular on the problem of inferring transcription factor activity when the concentration of active protein cannot easily be measured. We show how the uncertainty in the inferred transcription factor activity can be integrated out in order to derive a likelihood function that can be used for the estimation of regulatory model parameters. An advantage of our approach is that we avoid the use of a coarse-grained discretization of continuous-time functions, which would lead to a large number of additional parameters to be estimated. We develop efficient exact and approximate inference schemes, which are much more efficient than competing sampling-based schemes and therefore provide us with a practical toolkit for model-based inference.\ \ **Availability:** The software and data for recreating all the experiments in this paper is available in MATLAB from \ \ **Contact:** Neil Lawrence} }
Endnote
%0 Conference Paper %T Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities %A Pei Gao %A Antti Honkela %A Magnus Rattray %A Neil D. Lawrence %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-gao-latent08 %I PMLR %J Proceedings of Machine Learning Research %P 0--0 %R 10.1093/bioinformatics/btn278 %U http://inverseprobability.com %V %W PMLR %X **Motivation:** Inference of *latent chemical species* in biochemical interaction networks is a key problem in estimation of the structure and parameters of the genetic, metabolic and protein interaction networks that underpin all biological processes. We present a framework for Bayesian marginalisation of these latent chemical species through Gaussian process priors.\ \ **Results:** We demonstrate our general approach on three different biological examples of single input motifs, including both activation and repression of transcription. We focus in particular on the problem of inferring transcription factor activity when the concentration of active protein cannot easily be measured. We show how the uncertainty in the inferred transcription factor activity can be integrated out in order to derive a likelihood function that can be used for the estimation of regulatory model parameters. An advantage of our approach is that we avoid the use of a coarse-grained discretization of continuous-time functions, which would lead to a large number of additional parameters to be estimated. We develop efficient exact and approximate inference schemes, which are much more efficient than competing sampling-based schemes and therefore provide us with a practical toolkit for model-based inference.\ \ **Availability:** The software and data for recreating all the experiments in this paper is available in MATLAB from \ \ **Contact:** Neil Lawrence
RIS
TY - CPAPER TI - Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities AU - Pei Gao AU - Antti Honkela AU - Magnus Rattray AU - Neil D. Lawrence BT - PY - DA - ED - ID - pmlr-v-gao-latent08 PB - PMLR SP - 0 DP - PMLR EP - 0 DO - 10.1093/bioinformatics/btn278 L1 - UR - http://inverseprobability.com/publications/gao-latent08.html AB - **Motivation:** Inference of *latent chemical species* in biochemical interaction networks is a key problem in estimation of the structure and parameters of the genetic, metabolic and protein interaction networks that underpin all biological processes. We present a framework for Bayesian marginalisation of these latent chemical species through Gaussian process priors.\ \ **Results:** We demonstrate our general approach on three different biological examples of single input motifs, including both activation and repression of transcription. We focus in particular on the problem of inferring transcription factor activity when the concentration of active protein cannot easily be measured. We show how the uncertainty in the inferred transcription factor activity can be integrated out in order to derive a likelihood function that can be used for the estimation of regulatory model parameters. An advantage of our approach is that we avoid the use of a coarse-grained discretization of continuous-time functions, which would lead to a large number of additional parameters to be estimated. We develop efficient exact and approximate inference schemes, which are much more efficient than competing sampling-based schemes and therefore provide us with a practical toolkit for model-based inference.\ \ **Availability:** The software and data for recreating all the experiments in this paper is available in MATLAB from \ \ **Contact:** Neil Lawrence ER -
APA
Gao, P., Honkela, A., Rattray, M. & Lawrence, N.D.. (). Gaussian Process Modelling of Latent Chemical Species: Applications to Inferring Transcription Factor Activities. , in PMLR :0-0

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