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

裹住

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.

挖掘

捕鲸鱼叉

PAEAN

TEN

,Aus der forstlichen Geräthekammer,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