Name
Title | ||
|---|---|---|
A new insight into underlying disease mechanism through semi-parametric latent differential network model. |
Abstract | ||
|---|---|---|
The proposed latent variable differential network models allows for various data-types and thus are more flexible, which also provide deeper understanding of the unknown mechanism than that among the observed variables. Theoretical analysis, numerical simulation and real application all demonstrate the great advantages of the latent differential network modelling and thus are highly recommended. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2461-2 | BMC Bioinformatics |
Keywords | Field | DocType |
Adaptive estimation,Differential graphical model,Gaussian copula,Latent variable,Rank-based approach | Data mining,Biology,Copula (probability theory),Latent variable,Parametric statistics,Data type,Semiparametric model,Differential structure,Rate of convergence,Genetics,Network model | Journal |
Volume | Issue | ISSN |
19-S | 17 | 1471-2105 |
Citations | PageRank | References |
1 | 0.37 | 9 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yong He | 1 | 460 | 39.57 |
| Jiadong Ji | 2 | 6 | 1.16 |
| Lei Xie | 3 | 441 | 39.48 |
| Xinsheng Zhang | 4 | 2 | 2.10 |
| Fuzhong Xue | 5 | 6 | 1.83 |