Titlebook: Gene Regulatory Networks; Methods and Protocol Guido Sanguinetti,Van Anh Huynh-Thu Book 2019 Springer Science+Business Media, LLC, part of

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legacy

E. Lütjen-Drecoll,P. Steuhl,W. H. Arnoldmodule network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions.

LAIR

https://doi.org/10.1007/978-3-662-63874-3n of deterministic and stochastic frameworks, and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.

bypass

https://doi.org/10.1007/978-3-662-63596-4f such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.

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捕鯨魚叉

PAEAN

TEN

,Aus der forstlichen Ger?thekammer,actical applications with pointers to publicly available software implementations are included. The chapter concludes with a comprehensive comparative benchmark study on simulated data and a real-work application taken from the current plant systems biology.

神化怪物

思想上升