Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.
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Expectation propagation for large scale Bayesian inference of non-linear molecular networks from perturbation data.Dynamic Bayesian Network Modeling of the Interplay between EGFR and Hedgehog Signaling.Dynamic modeling and network approaches for omics time course data: overview of computational approaches and applications.Cross-talk between AMPK and EGFR dependent Signaling in Non-Small Cell Lung Cancer.An overview of bioinformatics methods for modeling biological pathways in yeast.Applications of Bayesian network models in predicting types of hematological malignancies.
P2860
Boosting probabilistic graphical model inference by incorporating prior knowledge from multiple sources.
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2013 nî lūn-bûn
@nan
2013 թուականի Յունիսին հրատարակուած գիտական յօդուած
@hyw
2013 թվականի հունիսին հրատարակված գիտական հոդված
@hy
2013年の論文
@ja
2013年論文
@yue
2013年論文
@zh-hant
2013年論文
@zh-hk
2013年論文
@zh-mo
2013年論文
@zh-tw
2013年论文
@wuu
name
Boosting probabilistic graphic ...... owledge from multiple sources.
@ast
Boosting probabilistic graphic ...... owledge from multiple sources.
@en
Boosting probabilistic graphic ...... owledge from multiple sources.
@nl
type
label
Boosting probabilistic graphic ...... owledge from multiple sources.
@ast
Boosting probabilistic graphic ...... owledge from multiple sources.
@en
Boosting probabilistic graphic ...... owledge from multiple sources.
@nl
prefLabel
Boosting probabilistic graphic ...... owledge from multiple sources.
@ast
Boosting probabilistic graphic ...... owledge from multiple sources.
@en
Boosting probabilistic graphic ...... owledge from multiple sources.
@nl
P2860
P1433
P1476
Boosting probabilistic graphic ...... nowledge from multiple sources
@en
P2093
Holger Fröhlich
Paurush Praveen
P2860
P304
P356
10.1371/JOURNAL.PONE.0067410
P407
P577
2013-06-24T00:00:00Z