GeneNetWeaver:<i>in silico</i>benchmark generation and performance profiling of network inference methods

Schaffter, Thomas; Marbach, Daniel; Floreano, Dario

doi:10.1093/bioinformatics/btr373

Cited by 494 publications

(467 citation statements)

References 41 publications

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“…As discussed in the Introduction of the paper, there is a wide range of algorithms designed to address this issue [41,45]. Furthermore, such interaction graphs could be deduced from the available reliable databases of biological networks.…”

Section: Resultsmentioning

confidence: 99%

“…It illustrates which resources are necessary to compute the origin, destination, conditions and delay of each timed local transition. To create as many models as possible that satisfy the given time series data, we add in the head of the rule the brackets { } between the predicates of the transition (see lines [41][42][43]. Then, we make sure that we keep exactly one transition for each change at a step T by component X.…”

Section: All Modelsmentioning

confidence: 99%

“…However, the models we learn can be applied to predict trajectories and thus attractors. Here, we use the influences graphs expected in the first problem as the background knowledge (inferred by the Gene Network Weaver [41]) to tackle the attractor prediction part of the challenge.…”

Section: Dream4mentioning

confidence: 99%

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Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

et al. 2017

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Abstract:Background: The modeling of Biological Regulatory Networks (BRNs) relies on background knowledge, deriving either from literature and/or the analysis of biological observations. However, with the development of high-throughput data, there is a growing need for methods that automatically generate admissible models. Methods: Our research aim is to provide a logical approach to infer BRNs based on given time series data and known influences among genes. Results: We propose a new methodology for models expressed through a timed extension of the automata networks (well suited for biological systems). The main purpose is to have a resulting network as consistent as possible with the observed datasets. Conclusion: The originality of our work is three-fold: (i) identifying the sign of the interaction; (ii) the direct integration of quantitative time delays in the learning approach; and (iii) the identification of the qualitative discrete levels that lead to the systems' dynamics. We show the benefits of such an automatic approach on dynamical biological models, the DREAM4(in silico) and DREAM8 (breast cancer) datasets, popular reverse-engineering challenges, in order to discuss the precision and the computational performances of our modeling method.

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Section: Resultsmentioning

confidence: 99%

Section: All Modelsmentioning

confidence: 99%

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Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

et al. 2017

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“…We applied PMI to the widely used DREAM3 challenge datasets for reconstructing gene regulatory networks (24), where the gold standard networks were from yeast and E. coli. The gene expression data were generated with the nonlinear ordinary differential equation systems (19,25). In this work, we used DREAM3 challenging data with sizes 10, 50, and 100 to construct the gene regulatory network by PMI-based PC (path consistency) algorithm, which is a greedy iteration algorithm for inferring networks and terminates until the network converges (16).…”

Section: Theoremmentioning

confidence: 99%

Part mutual information for quantifying direct associations in networks

Zhao

Zhou

Zhang

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

185

107

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Quantitatively identifying direct dependencies between variables is an important task in data analysis, in particular for reconstructing various types of networks and causal relations in science and engineering. One of the most widely used criteria is partial correlation, but it can only measure linearly direct association and miss nonlinear associations. However, based on conditional independence, conditional mutual information (CMI) is able to quantify nonlinearly direct relationships among variables from the observed data, superior to linear measures, but suffers from a serious problem of underestimation, in particular for those variables with tight associations in a network, which severely limits its applications. In this work, we propose a new concept, "partial independence," with a new measure, "part mutual information" (PMI), which not only can overcome the problem of CMI but also retains the quantification properties of both mutual information (MI) and CMI. Specifically, we first defined PMI to measure nonlinearly direct dependencies between variables and then derived its relations with MI and CMI. Finally, we used a number of simulated data as benchmark examples to numerically demonstrate PMI features and further real gene expression data from Escherichia coli and yeast to reconstruct gene regulatory networks, which all validated the advantages of PMI for accurately quantifying nonlinearly direct associations in networks.conditional mutual information | systems biology | network inference | conditional independence B ig data provide unprecedented information and opportunities to uncover ambiguous correlations among measured variables, but how to further infer direct associations, which means two variables are dependent given all of the remaining variables (1), quantitatively from those correlations or data remains a challenging task, in particular in science and engineering. For instance, distinguishing dependencies or direct associations between molecules is of great importance in reconstructing gene regulatory networks in biology (2-4), which can elucidate the molecular mechanisms of complex biological processes at a network level. Traditionally, correlation [e.g., the Pearson correlation coefficient (PCC)] is widely used to evaluate linear relations between the measured variables (2, 5), but it cannot distinguish indirect and direct associations due to only relying on the information of co-occurring events. Partial correlation (PC) avoids this problem by considering additional information of conditional events and can detect the direct associations. Thus, PC becomes one of the most widely used criteria to infer direct associations in various areas. As applications of PC to network reconstruction (6), recently Barzel and Barabási (7) proposed a dynamical correlation-based method to discriminate direct and indirect associations by silencing indirect effects in networks, and Feizi et al. (8) developed a network deconvolution method to distinguish direct dependencies by removing the combined effect of al...

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“…The nodes of GRNs are represented by genes and the edges between nodes represent interactions among genes 4 . Analysis of the GRNs helps in understanding the interactions among the genes and to identify the target genes for the breeding of tolerant varieties.…”

mentioning

confidence: 99%

Inferring Gene Regulatory Networks Using Kendall's Tau Correlation Coefficient and Identification of Salinity Stress Responsive Genes in Rice

S¹,

Meher²,

Pradhan³

et al. 2017

Current Science

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Salinity is one of the most common abiotic stresses that limit the production of rice. Since salinity stress tolerance is controlled by many genes, identification of these stress responsive genes as well as to understand the underlying mechanisms is of importance from breeding point of view. In this direction, the reverse engineering of gene regulatory networks has proven to be successful. In this study, we construct the gene regulatory network using Kendall's tau correlation coefficient, in order to identify the stress responsive genes. The proposed approach was tested on a rice microarray dataset and 18 key genes were identified. Most of these key genes were found to be involved directly or indirectly in salinity stress, as evidenced from the published literature. Gene ontology analysis further confirmed the involvement of the selected genes in ion binding, oxidation-reduction and phosphorylation activities. These identified genes can be targeted for breeding salt-tolerant varieties of rice.

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GeneNetWeaver:in silicobenchmark generation and performance profiling of network inference methods

Abstract: dario.floreano@epfl.ch.

Cited by 494 publications

References 41 publications

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

Modeling Delayed Dynamics in Biological Regulatory Networks from Time Series Data

Part mutual information for quantifying direct associations in networks

Inferring Gene Regulatory Networks Using Kendall's Tau Correlation Coefficient and Identification of Salinity Stress Responsive Genes in Rice

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