Vertragsgestaltung

Aderhold, Lutz; Koch, Raphael; Lenkaitis, Karlheinz

doi:10.5771/9783845262895

Cited by 1 publication

(3 citation statements)

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“…The network inference methods included in the comparison represent the function f at various levels of complexity. They are All-to-all (ATA) (Mombaerts et al, 2019), Gaussian Process Dynamical Models (GPDM) (Aalto et al, 2018), dynamical GEne Network Inference with Ensemble of trees (dynGENIE3) (Huynh-Thu and Geurts, 2018), Algorithm for Revealing Network Interactions (ARNI) (Casadiego et al, 2017) and Improved Chemical Model Averaging (iCheMA) (Aderhold et al, 2017). For details, we refer to publications presenting the methods.…”

Section: Network Inference Methodsmentioning

confidence: 99%

“…In the last few years, many computational methods based on a variety of mathematical paradigms have been developed to reconstruct GRNs from time-series data, and their performances assessed on relevant mathematical models (Casadiego et al, 2017;Aderhold et al, 2017;Marbach et al, 2012). However, experimentalists are still faced with difficult questions: Which method to use with an AA and JM are supported by University of Luxembourg internal researh projects PPPD (both) and OptBioSys (AA).…”

Section: Introductionmentioning

confidence: 99%

“…Realistic time-series datasets were generated from one rhythmic and five non-rhythmic models of gene regulatory networks that have been widely used as benchmarks in the literature (Aderhold et al, 2017;Prill et al, 2010). External interventions, i.e., gene deletion (knock-out) and chemical treatments, are explicitly simulated to provide a comprehensive picture of the performance of each algorithm under a range of experimental conditions.…”

Section: Introductionmentioning

confidence: 99%

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A multifactorial evaluation framework for gene regulatory network reconstruction

2019

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In the past years, many computational methods have been developed to infer the structure of gene regulatory networks from time-series data. However, the applicability and accuracy presumptions of such algorithms remain unclear due to experimental heterogeneity. This paper assesses the performance of recent and successful network inference strategies under a novel, multifactorial evaluation framework in order to highlight pragmatic tradeoffs in experimental design. The effects of data quantity and systems perturbations are addressed, thereby formulating guidelines for efficient resource management.Realistic data were generated from six widely used benchmark models of rhythmic and nonrhythmic gene regulatory systems with random perturbations mimicking the effect of gene knock-out or chemical treatments. Then, time-series data of increasing lengths were provided to five state-of-the-art network inference algorithms representing distinctive mathematical paradigms. The performances of such network reconstruction methodologies are uncovered under various experimental conditions. We report that the algorithms do not benefit equally from data increments. Furthermore, for rhythmic systems, it is more profitable for network inference strategies to be run on long time-series rather than short time-series with multiple perturbations. By contrast, for the non-rhythmic systems, increasing the number of perturbation experiments yielded better results than increasing the sampling frequency. We expect that future benchmark and algorithm design would integrate such multifactorial considerations to promote their widespread and conscientious usage.

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Section: Network Inference Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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