Advances to Bayesian network inference for generating causal networks from observational biological data

Yu, Jing; Smith, V. Anne; Wang, Paul P.; Hartemink, Alexander J.; Jarvis, Erich D.

doi:10.1093/bioinformatics/bth448

Cited by 602 publications

(438 citation statements)

References 21 publications

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“…Previous studies by Cantone et al [12] had suggested that DBNs (BANJO [43]) performed better than Table 2. Average area under the receiver operating characteristic curve (AUROC) and area under the precision-recall (AUPR) curve (in brackets) for each of the 100-gene DREAM4 networks using various inference algorithms.…”

Section: 21mentioning

confidence: 81%

How to infer gene networks from expression profiles, revisited

2011

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Inferring the topology of a gene-regulatory network (GRN) from genome-scale time-series measurements of transcriptional change has proved useful for disentangling complex biological processes. To address the challenges associated with this inference, a number of competing approaches have previously been used, including examples from information theory, Bayesian and dynamic Bayesian networks (DBNs), and ordinary differential equation (ODE) or stochastic differential equation. The performance of these competing approaches have previously been assessed using a variety of in silico and in vivo datasets. Here, we revisit this work by assessing the performance of more recent network inference algorithms, including a novel non-parametric learning approach based upon nonlinear dynamical systems. For larger GRNs, containing hundreds of genes, these non-parametric approaches more accurately infer network structures than do traditional approaches, but at significant computational cost. For smaller systems, DBNs are competitive with the non-parametric approaches with respect to computational time and accuracy, and both of these approaches appear to be more accurate than Granger causality-based methods and those using simple ODEs models.

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Section: 21mentioning

confidence: 81%

How to infer gene networks from expression profiles, revisited

2011

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“…Examples of such methods include principal component analysis (PCA) and hierarchical clustering (HC) to identify potentially co-expressed genes over the different combinatorial growth conditions. In addition, Bayesian network (BN) analysis can be used to infer regulatory linkages between mRNA abundances (Friedman, 2004;Hurley et al, 2012;Sachs et al, 2005;Yu et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Transcriptomic analysis reveals how a lack of potassium ions increases Sulfolobus acidocaldarius sensitivity to pH changes

et al. 2016

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Extremely acidophilic microorganisms (optimum growth pH of 3) maintain a near neutral cytoplasmic pH via several homeostatic mechanisms, including an inside positive membrane potential created by potassium ions. Transcriptomic responses to pH stress in the thermoacidophilic archaeon, Sulfolobus acidocaldarius were investigated by growing cells without added sodium and/or potassium ions at both optimal and sub-optimal pH. Culturing the cells in the absence of added sodium or potassium ions resulted in a reduced growth rate compared to full-salt conditions as well as 43 and 75 significantly different RNA transcript ratios, respectively. Differentially expressed RNA transcripts during growth in the absence of added sodium ions included genes coding for permeases, a sodium/proline transporter and electron transport proteins. In contrast, culturing without added potassium ions resulted in higher RNA transcripts for similar genes as a lack of sodium ions plus genes related to spermidine that has a general role in response to stress and a decarboxylase that potentially consumes protons. The greatest RNA transcript response occurred when S. acidocaldarius cells were grown in the absence of potassium and/or sodium at a sub-optimal pH. These adaptations included those listed above plus osmoregulated glucans and mechanosensitive channels that have previously been shown to respond to osmotic stress. In addition, data analyses revealed two co-expressed IclR family transcriptional regulator genes with a previously unknown role in the S. acidocaldarius pH stress response. Our study provides additional evidence towards the importance of potassium in acidophile growth at acidic pH.

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“…Although such comparison was reported before [36,37], an important fact was neglected, that gene expression time series data are far shorter from other biological time series data such as neuron data. For example, DBNs implementation is usually designed for hundreds or thousands of samples, e.g., the 2000 sample points of simulated data in [36,38]. Limitation of microarray experimental costs prohibits such technique from exploring gene expression data.…”

Section: Experiments On Synthetic Datamentioning

confidence: 99%

A Bayes Random Fields Approach for Integrative Large-Scale Regulatory Network Analysis

Yuan

2008

Journal of Integrative Bioinformatics

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SummaryWe present a Bayes-Random Fields framework which is capable of integrating unlimited data sources for discovering relevant network architecture of large-scale networks. The random field potential function is designed to impose a cluster constraint, teamed with a full Bayesian approach for incorporating heterogenous data sets. The probabilistic nature of our framework facilitates robust analysis in order to minimize the influence of noise inherent in the data on the inferred structure in a seamless and coherent manner. This is later proved in its applications to both large-scale synthetic data sets and Saccharomyces Cerevisiae data sets. The analytical and experimental results reveal the varied characteristic of different types of data and refelct their discriminative ability in terms of identifying direct gene interactions.

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Advances to Bayesian network inference for generating causal networks from observational biological data

Abstract: http://www.jarvislab.net/Bioinformatics/BNAdvances/

Cited by 602 publications

References 21 publications

How to infer gene networks from expression profiles, revisited

How to infer gene networks from expression profiles, revisited

Transcriptomic analysis reveals how a lack of potassium ions increases Sulfolobus acidocaldarius sensitivity to pH changes

A Bayes Random Fields Approach for Integrative Large-Scale Regulatory Network Analysis

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