Multinet Bayesian network models for large-scale transcriptome integration in computational medicine
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2013
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| Online Access: | http://hdl.handle.net/1721.1/77535 |
| _version_ | 1826200132904812544 |
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| author | Lin, Tiffany J |
| author2 | Gil Alterovitz. |
| author_facet | Gil Alterovitz. Lin, Tiffany J |
| author_sort | Lin, Tiffany J |
| collection | MIT |
| description | Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. |
| first_indexed | 2024-09-23T11:31:41Z |
| format | Thesis |
| id | mit-1721.1/77535 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:31:41Z |
| publishDate | 2013 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/775352019-04-12T16:11:15Z Multinet Bayesian network models for large-scale transcriptome integration in computational medicine Linking drugs and their side effects to gene mechanisms Lin, Tiffany J Gil Alterovitz. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012. Cataloged from PDF version of thesis. Includes bibliographical references (p. 30). Motivation: This work utilizes the closed loop Bayesian network framework for predictive medicine via integrative analysis of publicly available gene expression findings pertaining to various diseases and analyzes the results to determine which model, single net or multinet, is a more accurate predictor for determining disease status. Results: In general, it is suggested to use the multinet Bayesian network framework for predictive medicine instead of the single net Bayesian network, because for large numbers of samples and features, it is highly likely that it is the stronger predictor, and for smaller numbers of samples and features, if the multinet returns good results, it is likely to be a better predictor than the single net Bayesian network. by Tiffany J. Lin. M.Eng. 2013-03-01T15:27:10Z 2013-03-01T15:27:10Z 2012 2012 Thesis http://hdl.handle.net/1721.1/77535 826515154 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 30 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Lin, Tiffany J Multinet Bayesian network models for large-scale transcriptome integration in computational medicine |
| title | Multinet Bayesian network models for large-scale transcriptome integration in computational medicine |
| title_full | Multinet Bayesian network models for large-scale transcriptome integration in computational medicine |
| title_fullStr | Multinet Bayesian network models for large-scale transcriptome integration in computational medicine |
| title_full_unstemmed | Multinet Bayesian network models for large-scale transcriptome integration in computational medicine |
| title_short | Multinet Bayesian network models for large-scale transcriptome integration in computational medicine |
| title_sort | multinet bayesian network models for large scale transcriptome integration in computational medicine |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/77535 |
| work_keys_str_mv | AT lintiffanyj multinetbayesiannetworkmodelsforlargescaletranscriptomeintegrationincomputationalmedicine AT lintiffanyj linkingdrugsandtheirsideeffectstogenemechanisms |