Transcriptional network classifiers
Background Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biological processes are outcomes of regulatory gene interactions, this paper develo...
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
BioMed Central Ltd.
2010
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| Online Access: | http://hdl.handle.net/1721.1/52336 |
| _version_ | 1826196093734486016 |
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| author | Chang, Hsun-Hsien Ramoni, Marco F. |
| author2 | Harvard University--MIT Division of Health Sciences and Technology |
| author_facet | Harvard University--MIT Division of Health Sciences and Technology Chang, Hsun-Hsien Ramoni, Marco F. |
| author_sort | Chang, Hsun-Hsien |
| collection | MIT |
| description | Background
Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biological processes are outcomes of regulatory gene interactions, this paper develops a system biology approach to infer function-dependent transcriptional networks modulating phenotypic traits, which serve as a classifier to identify tissue states. Due to gene interactions taken into account in the analysis, we can achieve higher classification accuracy than existing methods.
Results
Our system biology approach is carried out by the Bayesian networks framework. The algorithm consists of two steps: gene filtering by Bayes factor followed by collinearity elimination via network learning. We validate our approach with two clinical data. In the study of lung cancer subtypes discrimination, we obtain a 25-gene classifier from 111 training samples, and the test on 422 independent samples achieves 95% classification accuracy. In the study of thoracic aortic aneurysm (TAA) diagnosis, 61 samples determine a 34-gene classifier, whose diagnosis accuracy on 33 independent samples achieves 82%. The performance comparisons with three other popular methods, PCA/LDA, PAM, and Weighted Voting, confirm that our approach yields superior classification accuracy and a more compact signature.
Conclusions
The system biology approach presented in this paper is able to infer function-dependent transcriptional networks, which in turn can classify biological samples with high accuracy. The validation of our classifier using clinical data demonstrates the promising value of our proposed approach for disease diagnosis. |
| first_indexed | 2024-09-23T10:20:45Z |
| format | Article |
| id | mit-1721.1/52336 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T10:20:45Z |
| publishDate | 2010 |
| publisher | BioMed Central Ltd. |
| record_format | dspace |
| spelling | mit-1721.1/523362022-09-26T17:21:08Z Transcriptional network classifiers Chang, Hsun-Hsien Ramoni, Marco F. Harvard University--MIT Division of Health Sciences and Technology Ramoni, Marco F. Ramoni, Marco F. Background Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. Since biological processes are outcomes of regulatory gene interactions, this paper develops a system biology approach to infer function-dependent transcriptional networks modulating phenotypic traits, which serve as a classifier to identify tissue states. Due to gene interactions taken into account in the analysis, we can achieve higher classification accuracy than existing methods. Results Our system biology approach is carried out by the Bayesian networks framework. The algorithm consists of two steps: gene filtering by Bayes factor followed by collinearity elimination via network learning. We validate our approach with two clinical data. In the study of lung cancer subtypes discrimination, we obtain a 25-gene classifier from 111 training samples, and the test on 422 independent samples achieves 95% classification accuracy. In the study of thoracic aortic aneurysm (TAA) diagnosis, 61 samples determine a 34-gene classifier, whose diagnosis accuracy on 33 independent samples achieves 82%. The performance comparisons with three other popular methods, PCA/LDA, PAM, and Weighted Voting, confirm that our approach yields superior classification accuracy and a more compact signature. Conclusions The system biology approach presented in this paper is able to infer function-dependent transcriptional networks, which in turn can classify biological samples with high accuracy. The validation of our classifier using clinical data demonstrates the promising value of our proposed approach for disease diagnosis. National Institutes of Health/National Human Genome Research Institute (R01HG003354) 2010-03-05T15:45:58Z 2010-03-05T15:45:58Z 2009-09 Article http://purl.org/eprint/type/JournalArticle 1471-2105 http://hdl.handle.net/1721.1/52336 Chang, Hsun-Hsien, and Marco Ramoni. “Transcriptional network classifiers.” BMC Bioinformatics 10.Suppl 9 (2009): S1. 19761563 en_US http://dx.doi.org/10.1186/1471-2105-10-S9-S1 BMC Bioinformatics Creative Commons Attribution http://creativecommons.org/licenses/by/2.0/ application/pdf BioMed Central Ltd. BioMed Central |
| spellingShingle | Chang, Hsun-Hsien Ramoni, Marco F. Transcriptional network classifiers |
| title | Transcriptional network classifiers |
| title_full | Transcriptional network classifiers |
| title_fullStr | Transcriptional network classifiers |
| title_full_unstemmed | Transcriptional network classifiers |
| title_short | Transcriptional network classifiers |
| title_sort | transcriptional network classifiers |
| url | http://hdl.handle.net/1721.1/52336 |
| work_keys_str_mv | AT changhsunhsien transcriptionalnetworkclassifiers AT ramonimarcof transcriptionalnetworkclassifiers |