Open Access
Peer-Reviewed
Original Research
Sensitivity and Specificity of Inferring Genetic
Abstract
In this paper we perform a study of the performance of the VBEM algorithm proposed in [19]. The VBEM is a Bayesian approach for reconstructing gene regulat ory networks (GRNs) based on microarray data. We focus on a variable selection formulation and devel op a solution by a variational Bayes Expecta- tion Maximization (VBEM) learning rule. The major advantag e of the VBEM solution over Monte Carlo sampling based approach is its lower computational complex ity. This makes it appealing for uncovering large networks. The suitability of the proposed algorithm t o infer large networks is studied in terms of its ROC curves.
Keywords
Gene networks
Microarray data
Bayesian inference
Vari ational Bayesian Expectation Maximization
ROC curves 1
Declarations & Ethics
Funding:
This research received academic dissemination support through ESCAP / JournalsHub publishing programs.
Conflicts of Interest:
The authors declare no competing financial or institutional interests.
Peer Review:
Double-blind peer reviewed by international subject specialists.
License:
Creative Commons Attribution 4.0 International (CC BY 4.0).
How to Cite This Article
APA / MLA / BibTeX
Tienda-Luna, et al. (2009). Sensitivity and Specificity of Inferring Genetic. IADIS International Journal on Computer Science and Information Systems, 4(1). https://doi.org/10.33965/ijcsis_2009_v4i1_06
Tienda-Luna, et al. "Sensitivity and Specificity of Inferring Genetic." IADIS International Journal on Computer Science and Information Systems, vol. 4, no. 1, 2009. https://doi.org/10.33965/ijcsis_2009_v4i1_06
Tienda-Luna, et al. "Sensitivity and Specificity of Inferring Genetic." IADIS International Journal on Computer Science and Information Systems 4, no. 1 (2009). https://doi.org/10.33965/ijcsis_2009_v4i1_06