Search

Motivation: Experimentally determined gene regulatory networks can be enriched by computational inference from high-throughput expression profiles. However, the prediction of regulatory interactions is severely impaired by indirect and spurious effects, particularly for eukaryotes. Recently, published methods report improved predictions by exploiting the a priori known targets of a regulator (its local topology) in addition to expression profiles. Results: We find that methods exploiting known targets show an unexpectedly high rate of false discoveries. This leads to inflated performance estimates and the prediction of an excessive number of new interactions for regulators with many known targets. These issues are hidden from common evaluation and cross-validation setups, which is due to Simpson’s paradox. We suggest a confidence score recalibration method (CoRe) that reduces the false discovery rate and enables a reliable performance estimation. Conclusions: CoRe considerably improves the results of network inference methods that exploit known targets. Predictions then display the biological process specificity of regulators more correctly and enable the inference of accurate genome-wide regulatory networks in eukaryotes. For yeast, we propose a network with more than 22 000 confident interactions. We point out that machine learning approaches outside of the area of network inference may be affected as well. Availability and implementation: Results, executable code and networks are available via our website http://www.bio.ifi.lmu.de/forschung/CoRe. Contact: robert.kueffner@helmholtz-muenchen.de Supplementary information: Supplementary data are available at Bioinformatics online.

Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.

Motivation: To improve the understanding of molecular regulation events, various approaches have been developed for deducing gene regulatory networks from mRNA expression data. Results: We present a new score for network inference, η2, that is derived from an analysis of variance. Candidate transcription factor:target gene (TF:TG) relationships are assumed more likely if the expression of TF and TG are mutually dependent in at least a subset of the examined experiments. We evaluate this dependency by η2, a non-parametric, non-linear correlation coefficient. It is fast, easy to apply and does not require the discretization of the input data. In the recent DREAM5 blind assessment, the arguably most comprehensive evaluation of inference methods, our approach based on η2 was rated the best performer on real expression compendia. It also performs better than methods tested in other recently published comparative assessments. About half of our predicted novel predictions are true interactions as estimated from qPCR experiments performed for DREAM5. Conclusions: The score η2 has a number of interesting features that enable the efficient detection of gene regulatory interactions. For most experimental setups, it is an interesting alternative to other measures of dependency such as Pearson's correlation or mutual information. Availability: See http://www2.bio.ifi.lmu.de/˜kueffner/anova.tar.gz for code and example data. Contact: kueffner@bio.ifi.lmu.de Supplementary information: Supplementary data are available at Bioinformatics online.

PurposeThis paper seeks to explore the necessary considerations in assessing and improving board performance in a Fortune 50 organization facing complex strategy execution challenges.Design/methodology/approachFace‐to‐face interviews with all non‐executive directors, the CEO and other senior executives were used to conduct a detailed evaluation of board performance, so as to highlight any potential areas of execution or governance risk and underperformance arising from gaps in competence, process or culture. In addition, analysis was carried out of overall board effectiveness and of individual board member contribution using detailed 360‐degree and aggregate questionnaires completed by board and key members of the management team. Further insight was gained from detailed benchmarking against global peers.FindingsThe work highlights that for a board to be a valuable differentiating asset to a company it is vital to balance the governance and oversight role of the board with a supportive and partnering relationship with executive management. This requires that a board should possess the necessary depth and breadth of experience to gain the respect and trust of the executive; that the chairman and CEO should define a contract that describes the nature of the relationship and the division of responsibilities between executive and board; and that the chairman should strive to create an open and challenging dynamic and constantly seek to improve board behaviors and interaction.Practical implicationsIt sets out clear objectives in building boards with complementary skill sets and experience and in embedding a culture of continuous self‐assessment and improvement.Originality/valueStrategic HR practitioners are encouraged to consider the value of the board to management as a differentiating asset for the organization.

Die algorithmische Systembiologie ist ein aktuelles Teilgebiet der Bioinformatik. Petrinetze [28] werden seit Jahrzehnten fur die Modellierung von Systemen und seit ca. 15 Jahren auch in der netzwerk-orientierten Bioinformatik [17, 27] verwendet. In der algorithmischen Systembiologie werden Petrinetze einerseits zur Reprasentation von biologischem Wissen in Form von qualitativen Netzwerkmodellen und andererseits fur die Analyse ihrer dynamischen Eigenschaften und ihre quantitative Simulation eingesetzt. Die Auswahl relevanter Teilnetze aus grosen qualitativen Netzen und ihre Umsetzung in quantitative Modelle erfordern neue methodische Ansatze.

The differential analysis of genes between microarrays from several experimental conditions or treatments routinely estimates which genes change significantly between groups. As genes are never regulated individually, observed behavior may be a consequence of changes in other genes. Existing approaches like co-expression analysis aim to resolve such patterns from a wide range of experiments. The knowledge of such a background set of experiments can be used to compute expected gene behavior based on known links. It is particularly interesting to detect previously unseen specific effects in other experiments. Here, a new method to spot genes deviating from expected behavior (PAttern DEviation SCOring--Padesco) is devised. It uses linear regression models learned from a background set to arrive at gene specific prediction accuracy distributions. For a given experiment, it is then decided whether each gene is predicted better or worse than expected. This provides a novel way to estimate the experiment specificity of each gene. We propose a validation procedure to estimate the detection of such specific candidates and show that these can be identified with an average accuracy of about 85%.

The differential analysis of genes between microarrays from several experimental conditions or treatments routinely estimates which genes change significantly between groups. As genes are never regulated individually observed behavior may be a consequence of changes in other genes. Existing approaches like co-expression analysis aim to resolve such patterns from a wide range of experiments. The knowledge of such a background set of experiments can be used to compute expected gene behavior based on known links. It is particularly interesting to detect previously unseen specific effects in other experiments. Here, a new method to spot genes deviating from expected behavior (PAttern DEviation SCOring - Padesco ) is devised. It uses linear regression models learned from a background set to arrive at gene specific prediction accuracy distributions. For a given experiment it is then decided whether each gene is predicted better or worse than expected. This provides a novel way to estimate the experiment specificityof each gene. We propose a validation procedure to estimate the detection of such specific candidates and show that these can be identified with an average accuracy of about 85 percent.