Stochastic dynamic modeling of short gene expression time-series data.
about
Short time-series microarray analysis: methods and challenges.Hybrid-controlled neurofuzzy networks analysis resulting in genetic regulatory networks reconstruction.Reverse engineering sparse gene regulatory networks using cubature kalman filter and compressed sensing.Identifying time-delayed gene regulatory networks via an evolvable hierarchical recurrent neural network.Regularized EM algorithm for sparse parameter estimation in nonlinear dynamic systems with application to gene regulatory network inference.An overview of the statistical methods used for inferring gene regulatory networks and protein-protein interaction networksPower-rate synchronization of coupled genetic oscillators with unbounded time-varying delay.Distinction immune genes of hepatitis-induced heptatocellular carcinoma.A Novel Data-Driven Boolean Model for Genetic Regulatory Networks
P2860
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P2860
Stochastic dynamic modeling of short gene expression time-series data.
description
2008 nî lūn-bûn
@nan
2008 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2008 թվականի մարտին հրատարակված գիտական հոդված
@hy
2008年の論文
@ja
2008年論文
@yue
2008年論文
@zh-hant
2008年論文
@zh-hk
2008年論文
@zh-mo
2008年論文
@zh-tw
2008年论文
@wuu
name
Stochastic dynamic modeling of short gene expression time-series data.
@ast
Stochastic dynamic modeling of short gene expression time-series data.
@en
type
label
Stochastic dynamic modeling of short gene expression time-series data.
@ast
Stochastic dynamic modeling of short gene expression time-series data.
@en
prefLabel
Stochastic dynamic modeling of short gene expression time-series data.
@ast
Stochastic dynamic modeling of short gene expression time-series data.
@en
P2093
P356
P1476
Stochastic dynamic modeling of short gene expression time-series data.
@en
P2093
P356
10.1109/TNB.2008.2000149
P577
2008-03-01T00:00:00Z