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Learning mixed graphical models with separate sparsity parameters and stability-based model selection

Sedgewick, AJ and Shi, I and Donovan, RM and Benos, PV (2016) Learning mixed graphical models with separate sparsity parameters and stability-based model selection. BMC Bioinformatics, 17 (5).

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Abstract

© 2016 Sedgewick et al. Background: Mixed graphical models (MGMs) are graphical models learned over a combination of continuous and discrete variables. Mixed variable types are common in biomedical datasets. MGMs consist of a parameterized joint probability density, which implies a network structure over these heterogeneous variables. The network structure reveals direct associations between the variables and the joint probability density allows one to ask arbitrary probabilistic questions on the data. This information can be used for feature selection, classification and other important tasks. Results: We studied the properties of MGM learning and applications of MGMs to high-dimensional data (biological and simulated). Our results show that MGMs reliably uncover the underlying graph structure, and when used for classification, their performance is comparable to popular discriminative methods (lasso regression and support vector machines). We also show that imposing separate sparsity penalties for edges connecting different types of variables significantly improves edge recovery performance. To choose these sparsity parameters, we propose a new efficient model selection method, named Stable Edge-specific Penalty Selection (StEPS). StEPS is an expansion of an earlier method, StARS, to mixed variable types. In terms of edge recovery, StEPS selected MGMs outperform those models selected using standard techniques, including AIC, BIC and cross-validation. In addition, we use a heuristic search that is linear in size of the sparsity value search space as opposed to the cubic grid search required by other model selection methods. We applied our method to clinical and mRNA expression data from the Lung Genomics Research Consortium (LGRC) and the learned MGM correctly recovered connections between the diagnosis of obstructive or interstitial lung disease, two diagnostic breathing tests, and cigarette smoking history. Our model also suggested biologically relevant mRNA markers that are linked to these three clinical variables. Conclusions: MGMs are able to accurately recover dependencies between sets of continuous and discrete variables in both simulated and biomedical datasets. Separation of sparsity penalties by edge type is essential for accurate network edge recovery. Furthermore, our stability based method for model selection determines sparsity parameters faster and more accurately (in terms of edge recovery) than other model selection methods. With the ongoing availability of comprehensive clinical and biomedical datasets, MGMs are expected to become a valuable tool for investigating disease mechanisms and answering an array of critical healthcare questions.


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Details

Item Type: Article
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Sedgewick, AJajs206@pitt.eduAJS206
Shi, Iivs5@pitt.eduIVS5
Donovan, RMdonovanr@pitt.eduDONOVANR
Benos, PVbenos@pitt.eduBENOS
Date: 6 June 2016
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: BMC Bioinformatics
Volume: 17
Number: 5
DOI or Unique Handle: 10.1186/s12859-016-1039-0
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computational Biology
School of Medicine > Computational and Systems Biology
Swanson School of Engineering > Bioengineering
Refereed: Yes
Date Deposited: 25 Jul 2016 13:47
Last Modified: 26 Jan 2019 13:55
URI: http://d-scholarship.pitt.edu/id/eprint/28661

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