Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data.
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A Machine Learning Approach to Predict Gene Regulatory Networks in Seed Development in Arabidopsis.Reconstruction of the temporal signaling network in Salmonella-infected human cellslpNet: a linear programming approach to reconstruct signal transduction networks.HiDi: an efficient reverse engineering schema for large-scale dynamic regulatory network reconstruction using adaptive differentiation.Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity
P2860
Dynamic probabilistic threshold networks to infer signaling pathways from time-course perturbation data.
description
2014 nî lūn-bûn
@nan
2014 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2014 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2014年の論文
@ja
2014年論文
@yue
2014年論文
@zh-hant
2014年論文
@zh-hk
2014年論文
@zh-mo
2014年論文
@zh-tw
2014年论文
@wuu
name
Dynamic probabilistic threshol ...... time-course perturbation data.
@ast
Dynamic probabilistic threshol ...... time-course perturbation data.
@en
type
label
Dynamic probabilistic threshol ...... time-course perturbation data.
@ast
Dynamic probabilistic threshol ...... time-course perturbation data.
@en
prefLabel
Dynamic probabilistic threshol ...... time-course perturbation data.
@ast
Dynamic probabilistic threshol ...... time-course perturbation data.
@en
P2860
P356
P1433
P1476
Dynamic probabilistic threshol ...... time-course perturbation data.
@en
P2860
P2888
P356
10.1186/1471-2105-15-250
P577
2014-07-22T00:00:00Z
P5875
P6179
1021319008