Inferring gene regulatory networks from time series data using the minimum description length principle
By Wentao Zhao, Erchin Serpedin, Edward R. Dougherty. Bioinform., 2006
375 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/49e6349ac4dba3683d6e6629e47f279fa428319a
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Pierre Geurts
- Louis Wehenkel
- Vân Anh Huynh-Thu
- Daniel Eaton
- Kevin P. Murphy
- Guanghua Xiao
- Yik-Chung Wu
- Qinliang Su
In Molecular Biology
- Vân Anh Huynh-Thu
- Daniel Eaton
- Yang Xie
- Louis Wehenkel
- Kevin P. Murphy
- Luc Girard
- Guanghua Xiao
- Yik-Chung Wu
Citations in the corpus
- Distributed Estimation of Variance in Gaussian Graphical Model via Belief Propagation: Accuracy Analysis and Improvement (2015, background): “…physical processes and phenomenons can be modeled by Gaussian graphical models, they are widely used in applications ranging from computer vision [3], [4], image processing [5], oceanography [6], gene regulatory networks [7], cellular communication networks [8] to wireless sensor networks [9]–[11].”
- Comparing Statistical Methods for Constructing Large Scale Gene Networks (2012, methodology): “Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1) GeneNet, WGCNA (Weighted Correlation Network Analysis), and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) performed well in constructing the global network structur”
- Belief net structure learning from uncertain interventions (2007, methodology): “…been used to learn belief networks from this data, including multiple restart simulated annealing in the space of DAGs (Sachs et al., 2005), Metropolis- Hastings in the space of node orderings (Werhli et al., 2006), and equi-energy sampling in the space of node orderings (Ellis and Wong, 2006).”
- Inferring Regulatory Networks from Expression Data Using Tree-Based Methods (2010, background): “They are however also less informative for the prediction of edge directionality [3,26,27] and therefore make the regulatory network inference task more challenging.”