Influence of Prior Knowledge in Constraint-Based Learning of Gene Regulatory Networks

2011 ◽  
Vol 8 (1) ◽  
pp. 130-142 ◽  
Author(s):  
Mehmet Tan ◽  
Mohammed Alshalalfa ◽  
Reda Alhajj ◽  
Faruk Polat
Keyword(s):  
Prior Knowledge ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Gene Regulatory

scholarly journals Reconstructing Gene Regulatory Networks with Bayesian Networks by Combining Expression Data with Multiple Sources of Prior Knowledge

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Adriano V Werhli ◽  
Dirk Husmeier
Keyword(s):  
Prior Knowledge ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Reconstruction Error ◽  
Expression Data ◽  
Multiple Sources ◽  
Promoter Regions ◽  
Experimental Conditions ◽  
Microarray Expression Data ◽  
Gene Regulatory

There have been various attempts to reconstruct gene regulatory networks from microarray expression data in the past. However, owing to the limited amount of independent experimental conditions and noise inherent in the measurements, the results have been rather modest so far. For this reason it seems advisable to include biological prior knowledge, related, for instance, to transcription factor binding locations in promoter regions or partially known signalling pathways from the literature. In the present paper, we consider a Bayesian approach to systematically integrate expression data with multiple sources of prior knowledge. Each source is encoded via a separate energy function, from which a prior distribution over network structures in the form of a Gibbs distribution is constructed. The hyperparameters associated with the different sources of prior knowledge, which measure the influence of the respective prior relative to the data, are sampled from the posterior distribution with MCMC. We have evaluated the proposed scheme on the yeast cell cycle and the Raf signalling pathway. Our findings quantify to what extent the inclusion of independent prior knowledge improves the network reconstruction accuracy, and the values of the hyperparameters inferred with the proposed scheme were found to be close to optimal with respect to minimizing the reconstruction error.

scholarly journals Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses

2020 ◽  
Author(s):  
Aurelien Dugourd ◽  
Christoph Kuppe ◽  
Marco Sciacovelli ◽  
Enio Gjerga ◽  
Kristina B. Emdal ◽  
...  
Keyword(s):  
Prior Knowledge ◽  
Gene Regulatory Networks ◽  
Causal Reasoning ◽  
Regulatory Networks ◽  
Omics Data ◽  
Metabolomics Data ◽  
Nucleoside Metabolism ◽  
Gene Regulatory ◽  
The Impact ◽  
Stat Pathway

AbstractMulti-omics datasets can provide molecular insights beyond the sum of individual omics. Diverse tools have been recently developed to integrate such datasets, but there are limited strategies to systematically extract mechanistic hypotheses from them. Here, we present COSMOS (Causal Oriented Search of Multi-Omics Space), a method that integrates phosphoproteomics, transcriptomics, and metabolics datasets. COSMOS combines extensive prior knowledge of signaling, metabolic, and gene regulatory networks with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS provides mechanistic hypotheses for experimental observations across multi-omics datasets. We applied COSMOS to a dataset comprising transcriptomics, phosphoproteomics, and metabolomics data from healthy and cancerous tissue from nine renal cell carcinoma patients. We used COSMOS to generate novel hypotheses such as the impact of Androgen Receptor on nucleoside metabolism and the influence of the JAK-STAT pathway on propionyl coenzyme A production. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omics studies.Abstract Figure

scholarly journals Integration of a Computational Pipeline for Dynamic Inference of Gene Regulatory Networks in Single Cells

2019 ◽  
Author(s):  
Kyung Dae Ko ◽  
Stefania Dell’Orso ◽  
Aster H. Juan ◽  
Vittorio Sartorelli
Keyword(s):  
Prior Knowledge ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Developmental Stages ◽  
Individual Cell ◽  
Single Cells ◽  
Rna Seq ◽  
Gene Regulatory ◽  
Molecular Expression

SUMMARYSingle-cell RNA-seq permits the characterization of the molecular expression states of individual cells. Several methods have been developed to spatially and temporally resolve individual cell populations. However, these methods are not always integrated and some of them are constrained by prior knowledge. Here, we present an integrated pipeline for inference of gene regulatory networks. The pipeline does not rely on prior knowledge, it improves inference accuracy by integrating signatures from different data dimensions and facilitates tracing variation of gene expression by visualizing gene-interacting patterns of co-expressed gene regulatory networks at distinct developmental stages.

scholarly journals Combining gene expression data and prior knowledge for inferring gene regulatory networks via Bayesian networks using structural restrictions

2019 ◽  
Vol 18 (3) ◽  
Author(s):  
Luis M. de Campos ◽  
Andrés Cano ◽  
Javier G. Castellano ◽  
Serafín Moral
Keyword(s):  
Gene Expression ◽  
Bayesian Networks ◽  
Prior Knowledge ◽  
Gene Expression Data ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Cellular Systems ◽  
Expression Data ◽  
Starting Point ◽  
Gene Regulatory

Abstract Gene Regulatory Networks (GRNs) are known as the most adequate instrument to provide a clear insight and understanding of the cellular systems. One of the most successful techniques to reconstruct GRNs using gene expression data is Bayesian networks (BN) which have proven to be an ideal approach for heterogeneous data integration in the learning process. Nevertheless, the incorporation of prior knowledge has been achieved by using prior beliefs or by using networks as a starting point in the search process. In this work, the utilization of different kinds of structural restrictions within algorithms for learning BNs from gene expression data is considered. These restrictions will codify prior knowledge, in such a way that a BN should satisfy them. Therefore, one aim of this work is to make a detailed review on the use of prior knowledge and gene expression data to inferring GRNs from BNs, but the major purpose in this paper is to research whether the structural learning algorithms for BNs from expression data can achieve better outcomes exploiting this prior knowledge with the use of structural restrictions. In the experimental study, it is shown that this new way to incorporate prior knowledge leads us to achieve better reverse-engineered networks.

scholarly journals GENE REGULATORY NETWORK RECONSTRUCTION BY BAYESIAN INTEGRATION OF PRIOR KNOWLEDGE AND/OR DIFFERENT EXPERIMENTAL CONDITIONS

2008 ◽  
Vol 06 (03) ◽  
pp. 543-572 ◽  
Author(s):  
ADRIANO V. WERHLI ◽  
DIRK HUSMEIER
Keyword(s):  
Prior Knowledge ◽  
Gene Regulatory Networks ◽  
Regulatory Networks ◽  
Coupling Scheme ◽  
Data Sets ◽  
Network Structures ◽  
Learning Networks ◽  
Experimental Conditions ◽  
Related Data ◽  
Gene Regulatory

There have been various attempts to improve the reconstruction of gene regulatory networks from microarray data by the systematic integration of biological prior knowledge. Our approach is based on pioneering work by Imoto et al.11 where the prior knowledge is expressed in terms of energy functions, from which a prior distribution over network structures is obtained in the form of a Gibbs distribution. The hyperparameters of this distribution represent the weights associated with the prior knowledge relative to the data. We have derived and tested a Markov chain Monte Carlo (MCMC) scheme for sampling networks and hyperparameters simultaneously from the posterior distribution, thereby automatically learning how to trade off information from the prior knowledge and the data. We have extended this approach to a Bayesian coupling scheme for learning gene regulatory networks from a combination of related data sets, which were obtained under different experimental conditions and are therefore potentially associated with different active subpathways. The proposed coupling scheme is a compromise between (1) learning networks from the different subsets separately, whereby no information between the different experiments is shared; and (2) learning networks from a monolithic fusion of the individual data sets, which does not provide any mechanism for uncovering differences between the network structures associated with the different experimental conditions. We have assessed the viability of all proposed methods on data related to the Raf signaling pathway, generated both synthetically and in cytometry experiments.

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