A Robust Procedure For Gaussian Graphical Model Search From Microarray Data With p Larger Than n
The Journal of Machine Learning Research
Towards scalable and data efficient learning of Markov boundaries
International Journal of Approximate Reasoning
Inferring time-varying network topologies from gene expression data
EURASIP Journal on Bioinformatics and Systems Biology
Inferring biomolecular interaction networks based on convex optimization
Computational Biology and Chemistry
EURASIP Journal on Bioinformatics and Systems Biology
Conditional-mean least-squares fitting of Gaussian Markov random fields to Gaussian fields
Computational Statistics & Data Analysis
Modeling genetic networks: comparison of static and dynamic models
Proceedings of the 2007 Summer Computer Simulation Conference
The pattern memory of gene-protein networks
AI Communications - Network Analysis in Natural Sciences and Engineering
Journal of Biomedical Informatics
Tests for Gaussian graphical models
Computational Statistics & Data Analysis
An improved shrinkage estimator to infer regulatory networks with Gaussian graphical models
Proceedings of the 2009 ACM symposium on Applied Computing
EvoBIO '09 Proceedings of the 7th European Conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Reverse engineering of gene regulatory networks: a comparative study
EURASIP Journal on Bioinformatics and Systems Biology - Special issue on network structure and biological function: Reconstruction, modelling, and statistical approaches
Using modified Lasso regression to learn large undirected graphs in a probabilistic framework
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Efficient Markov network structure discovery using independence tests
Journal of Artificial Intelligence Research
Gene function classification using NCI-60 cell line gene expression profiles
Computational Biology and Chemistry
The Journal of Machine Learning Research
Modeling genetic networks: comparison of static and dynamic models
EvoBIO'07 Proceedings of the 5th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Biological network inference using redundancy analysis
BIRD'07 Proceedings of the 1st international conference on Bioinformatics research and development
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Parallel information theory based construction of gene regulatory networks
HiPC'08 Proceedings of the 15th international conference on High performance computing
Learning Gaussian graphical models of gene networks with false discovery rate control
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Application notes: data mining in cancer research
IEEE Computational Intelligence Magazine
A hyper-graph approach for analyzing transcriptional networks in breast cancer
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
Crosstalk measures for analyzing biological networks in breast cancer
Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology
CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics
Algorithms and theory of computation handbook
A Markov-Blanket-Based Model for Gene Regulatory Network Inference
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Inferring Contagion in Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Qualitative reasoning on systematic gene perturbation experiments
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
A hybrid algorithm to infer genetic networks
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
CompLife'06 Proceedings of the Second international conference on Computational Life Sciences
GSGS: A Computational Approach to Reconstruct Signaling Pathway Structures from Gene Sets
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Qualitative Reasoning for Biological Network Inference from Systematic Perturbation Experiments
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
RECOMB'13 Proceedings of the 17th international conference on Research in Computational Molecular Biology
A comparative study of covariance selection models for the inference of gene regulatory networks
Journal of Biomedical Informatics
Edge detection in sparse Gaussian graphical models
Computational Statistics & Data Analysis
Sub-local constraint-based learning of Bayesian networks using a joint dependence criterion
The Journal of Machine Learning Research
| Hi-index | 3.84 |
Motivation: Genetic networks are often described statistically using graphical models (e.g. Bayesian networks). However, inferring the network structure offers a serious challenge in microarray analysis where the sample size is small compared to the number of considered genes. This renders many standard algorithms for graphical models inapplicable, and inferring genetic networks an 'ill-posed' inverse problem. Methods: We introduce a novel framework for small-sample inference of graphical models from gene expression data. Specifically, we focus on the so-called graphical Gaussian models (GGMs) that are now frequently used to describe gene association networks and to detect conditionally dependent genes. Our new approach is based on (1) improved (regularized) small-sample point estimates of partial correlation, (2) an exact test of edge inclusion with adaptive estimation of the degree of freedom and (3) a heuristic network search based on false discovery rate multiple testing. Steps (2) and (3) correspond to an empirical Bayes estimate of the network topology. Results: Using computer simulations, we investigate the sensitivity (power) and specificity (true negative rate) of the proposed framework to estimate GGMs from microarray data. This shows that it is possible to recover the true network topology with high accuracy even for small-sample datasets. Subsequently, we analyze gene expression data from a breast cancer tumor study and illustrate our approach by inferring a corresponding large-scale gene association network for 3883 genes. Availability: The authors have implemented the approach in the R package 'GeneTS' that is freely available from http://www.stat.uni-muenchen.de/~strimmer/genets/, from the R archive (CRAN) and from the Bioconductor website. Contact: korbinian.strimmer@lmu.de