Combining Multiple Results of a Reverse‐Engineering Algorithm: Application to the DREAM Five‐Gene Network Challenge

Marbach, Daniel; Mattiussi, Claudio; Floreano, Dario

doi:10.1111/j.1749-6632.2008.03945.x

Cited by 30 publications

(21 citation statements)

References 20 publications

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“…For example, edges can be aggregated by using a majority vote (an edge is included only if it is predicted by more than a minimum number of groups) or using a scheme that weighs edges predicted by each team according to the team’s Prediction Score or Overall Score. Other methods of aggregation have also been proposed (15, 16). Because only some of the nodes in the provided PKN were measured or manipulated in the HepG2 cell line data, we asked participants to submit HepG1 networks containing only the observable nodes (ligands, measured phosphoproteins, and molecules targeted by the inhibitors) and the edges linking them.…”

Section: Crowdsourcing As a Strategy For Signaling Network Reconstrucmentioning

confidence: 99%

Crowdsourcing Network Inference: The DREAM Predictive Signaling Network Challenge Meeting Information: The DREAM4 Predictive Signaling Network Challenge took place in the summer of 2009. Results were presented at the DREAM4 conference, December 2009, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.

et al. 2011

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Computational analyses of systematic measurements on the states and activities of signaling proteins (as captured by phosphoproteomic data, for example) have the potential to uncover uncharacterized protein-protein interactions and to identify the subset that are important for cellular response to specific biological stimuli. However, inferring mechanistically plausible protein signaling networks (PSNs) from phosphoproteomics data is a difficult task, owing in part to the lack of sufficiently comprehensive experimental measurements, the inherent limitations of network inference algorithms, and a lack of standards for assessing the accuracy of inferred PSNs. A case study in which 12 research groups inferred PSNs from a phosphoproteomics data set demonstrates an assessment of inferred PSNs on the basis of the accuracy of their predictions. The concurrent prediction of the same previously unreported signaling interactions by different participating teams suggests relevant validation experiments and establishes a framework for combining PSNs inferred by multiple research groups into a composite PSN. We conclude that crowdsourcing the construction of PSNs—that is, outsourcing the task to the interested community—may be an effective strategy for network inference.

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Section: Crowdsourcing As a Strategy For Signaling Network Reconstrucmentioning

confidence: 99%

Crowdsourcing Network Inference: The DREAM Predictive Signaling Network Challenge Meeting Information: The DREAM4 Predictive Signaling Network Challenge took place in the summer of 2009. Results were presented at the DREAM4 conference, December 2009, The Broad Institute of MIT and Harvard, Cambridge, Massachusetts.

et al. 2011

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“…Friedman et al [46] used a resampling approach of a Bayesian network reconstruction algorithm to assess the confidence of inferred parameters. Additionally, Marbach et al [47] showed that a resampling approach applied to a genetic algorithm for network inference was a top performering method in the DREAM2 five-gene network challenge. We show that by using a resampling approach to generate ensembles of networks with our network inference pipeline we can improve the accuracy of our topology predictions.…”

Section: Introductionmentioning

confidence: 99%

DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models

et al. 2010

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BackgroundCurrent technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.MethodologyWe investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test–based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations.Conclusion/SignificanceOur t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/.

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“…We therefore chose a consensus approach to predict an ensemble of networks. Consensus prediction approaches have been successfully applied to network reconstruction before [31]. We carry out 100 reconstruction runs with different parameter settings (Table 1) and random seeds.…”

Section: Methodsmentioning

confidence: 99%

Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization

et al. 2010

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BackgroundThe recent DREAM4 blind assessment provided a particularly realistic and challenging setting for network reverse engineering methods. The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial perturbations.Methodology and Principal FindingsWe inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). This completely automated approach correctly reconstructed networks with cycles as well as oscillating network motifs. PNFL was evaluated as the best performer on DREAM4 in silico networks of size 10 with an area under the precision-recall curve (AUPR) of 81%. Besides topology, we inferred a range of additional mechanistic details with good reliability, e.g. distinguishing activation from inhibition as well as dependent from independent regulation. Our models also performed well on new experimental conditions such as double knockout mutations that were not included in the provided datasets.ConclusionsThe inference of biological networks substantially benefits from methods that are expressive enough to deal with diverse datasets in a unified way. At the same time, overly complex approaches could generate multiple different models that explain the data equally well. PNFL appears to strike the balance between expressive power and complexity. This also applies to the intuitive representation of PNFL models combining a straightforward graphical notation with colloquial fuzzy parameters.

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Combining Multiple Results of a Reverse‐Engineering Algorithm: Application to the DREAM Five‐Gene Network Challenge

Cited by 30 publications

References 20 publications

DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models

Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization

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