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| LEADER |
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| 001 |
in00002784528 |
| 005 |
20150922150744.0 |
| 006 |
m fo d |
| 007 |
cr unu|||||||| |
| 008 |
121127s2012 txu obm 000 0 eng d |
| 035 |
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|a (OCoLC)ocn819409727
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| 035 |
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|a (OCoLC)819409727
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| 035 |
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|a (TxCM)http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9860
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| 040 |
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|a TXA
|c TXA
|d UtOrBLW
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| 049 |
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|a TXAM
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| 099 |
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|a 2011
|a Thesis
|a 1969.1/ETD-TAMU-2011-08-9860
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| 100 |
0 |
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|a Amina Noor.
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| 245 |
1 |
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|a Modeling gene regulatory networks from time series data using particle filtering /
|c by Amina Noor.
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| 264 |
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1 |
|a [College Station, Tex.] :
|b [Texas A&M University],
|c [2012]
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| 300 |
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|a 1 online resource.
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 500 |
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|a "Major Subject: Electrical Engineering"
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| 588 |
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|a Description from author supplied metadata (automated record created 2012-10-22 13:24:58).
|
| 502 |
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|b Master of Science
|c Texas A&M University
|d 2011
|o http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9860
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| 504 |
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|a Includes bibliographical references.
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| 516 |
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|a Text (Thesis)
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3 |
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|a This thesis considers the problem of learning the structure of gene regulatory networks using gene expression time series data. A more realistic scenario where the state space model representing a gene network evolves nonlinearly is considered while a linear model is assumed for the microarray data. To capture the nonlinearity, a particle filter based state estimation algorithm is studied instead of the contemporary linear approximation based approaches. The parameters signifying the regulatory relations among various genes are estimated online using a Kalman filter. Since a particular gene interacts with a few other genes only, the parameter vector is expected to be sparse. The state estimates delivered by the particle filter and the observed microarray data are then fed to a LASSO based least squares regression operation, which yields a parsimonious and efficient description of the regulatory network by setting the irrelevant coefficients to zero. The performance of the aforementioned algorithm is compared with Extended Kalman filtering (EKF), employing Mean Square Error as fidelity criterion using synthetic data and real biological data. Extensive computer simulations illustrate that the particle filter based gene network inference algorithm outperforms EKF and therefore, it can serve as a natural framework for modeling gene regulatory networks.
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| 500 |
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|a Electronic resource.
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| 650 |
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4 |
|a Major Electrical Engineering.
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| 653 |
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|a gene network modeling
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| 653 |
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|a lasso
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| 653 |
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|a particle filter
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| 700 |
1 |
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|a Serpedin, Erchin,
|e thesis advisor.
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| 700 |
1 |
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|a Nounou, Mohamed,
|e thesis advisor.
|
| 856 |
4 |
0 |
|u http://hdl.handle.net/1969.1/ETD-TAMU-2011-08-9860
|z Link to OAK Trust copy
|t 0
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| 994 |
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|a C0
|b TXA
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| 948 |
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|a cataloged
|b h
|c 2012/11/27
|d o
|e agaffey
|f 9:39:32 am
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| 999 |
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|a MARS
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| 999 |
f |
f |
|s 354ed9a7-3ccb-39e8-b311-4b83fe4b14d4
|i c52f7008-d236-357c-8324-a4d6202613bf
|t 0
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| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|d Available Online
|t 0
|e 2011 Thesis 1969.1/ETD-TAMU-2011-08-9860
|h Other scheme
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| 998 |
f |
f |
|a 2011 Thesis 1969.1/ETD-TAMU-2011-08-9860
|t 0
|l Available Online
|