| Type of Document |
Master's Thesis |
| Author |
Chen, Shuo
|
| Author's Email Address |
schen@vt.edu |
| URN |
etd-12152006-132023 |
| Title |
The Application of the Expectation-Maximization Algorithm to the Identification of Biological Models |
| Degree |
Master of Science |
| Department |
Electrical and Computer Engineering |
| Advisory Committee |
| Advisor Name |
Title |
| Baumann, William T. |
Committee Chair |
| Wang, Joseph |
Committee Member |
| Xuan, Jianhua Jason |
Committee Member |
|
| Keywords |
- Gene Regulatory Networks
- EM Algorithm
|
| Date of Defense |
2006-12-11 |
| Availability |
unrestricted |
Abstract
With the onset of large-scale gene expression profiling, many researchers have turned their attention toward biological process modeling and system identification. The abundance of data available, while inspiring, is also daunting to interpret. Following the initial work of Rangel et al., we propose a linear model for identifying the biological model behind the data and utilize a modification of the Expectation-Maximization algorithm for training it. With our model, we explore some commonly accepted assumptions concerning sampling, discretization, and state transformations. Also, we illuminate the model complexities and interpretation difficulties caused by unknown state transformations and propose some solutions for resolving these problems. Finally, we elucidate the advantages and limitations of our linear state-space model with simulated data from several nonlinear networks.
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| Files |
| Filename |
Size |
Approximate Download Time
(Hours:Minutes:Seconds)
|
| 28.8 Modem |
56K Modem |
ISDN (64 Kb) |
ISDN (128 Kb) |
Higher-speed Access |
| |
Final_Thesis.pdf |
3.04 Mb |
00:14:03 |
00:07:13 |
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