Using graph theory to analyze biological networks
Understanding complex systems often requires a bottom-up analysis towards a systems
biology approach. The need to investigate a system, not only as individual components but …
biology approach. The need to investigate a system, not only as individual components but …
Modeling heterogeneity in random graphs through latent space models: a selective review
Modeling heterogeneity in random graphs through latent space models: a selective review\*
Page 1 ESAIM: PROCEEDINGS AND SURVEYS, December 2014, Vol. 47, p. 55-74 F …
Page 1 ESAIM: PROCEEDINGS AND SURVEYS, December 2014, Vol. 47, p. 55-74 F …
[图书][B] Handbook of mixture analysis
Mixture models have been around for over 150 years, and they are found in many branches
of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide …
of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide …
The auxin signalling network translates dynamic input into robust patterning at the shoot apex
The plant hormone auxin is thought to provide positional information for patterning during
development. It is still unclear, however, precisely how auxin is distributed across tissues …
development. It is still unclear, however, precisely how auxin is distributed across tissues …
Consistency of maximum-likelihood and variational estimators in the stochastic block model
The stochastic block model (SBM) is a probabilistic model designed to describe
heterogeneous directed and undirected graphs. In this paper, we address the asymptotic …
heterogeneous directed and undirected graphs. In this paper, we address the asymptotic …
Classification and estimation in the stochastic blockmodel based on the empirical degrees
Abstract The Stochastic Blockmodel [16] is a mixture model for heterogeneous network data.
Unlike the usual statistical framework, new nodes give additional information about the …
Unlike the usual statistical framework, new nodes give additional information about the …
New consistent and asymptotically normal parameter estimates for random-graph mixture models
C Ambroise, C Matias - Journal of the Royal Statistical Society …, 2012 - academic.oup.com
Random-graph mixture models are very popular for modelling real data networks.
Parameter estimation procedures usually rely on variational approximations, either …
Parameter estimation procedures usually rely on variational approximations, either …
Finding Missing Interactions of the Arabidopsis thaliana Root Stem Cell Niche Gene Regulatory Network
Over the last few decades, the Arabidopsis thaliana root stem cell niche (RSCN) has
become a model system for the study of plant development and stem cell niche dynamics …
become a model system for the study of plant development and stem cell niche dynamics …
Parameter identifiability in a class of random graph mixture models
We prove identifiability of parameters for a broad class of random graph mixture models.
These models are characterized by a partition of the set of graph nodes into latent …
These models are characterized by a partition of the set of graph nodes into latent …
Powerful multiple testing of paired null hypotheses using a latent graph model
T Rebafka, É Roquain, F Villers - Electronic Journal of Statistics, 2022 - projecteuclid.org
In this paper, we explore the multiple testing problem of paired null hypotheses, for which
the data are collected on pairs of entities and tests have to be performed for each pair …
the data are collected on pairs of entities and tests have to be performed for each pair …