Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


ohiou1275926172.pdf (1.25 MB)

ETD Abstract Container

Abstract Header

Recovery and Analysis of Regulatory Networks from Expression Data Using Sums of Separable Functions

Botts, Ryan T.
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1275926172

Abstract Details

2010, Doctor of Philosophy (PhD), Ohio University, Mathematics (Arts and Sciences).
Many regulation networks and control systems may be modeled using systems of ordinary dierential equations, e.g. gene regulatory networks. These equations are functions of many variables and are usually unknown. It is possible to collect time course network expression data using equipment such as microarrays. The large number of components in these networks coupled with relatively small sets of data yields an ugly showing of the curse of dimensionality. Here we develop an alternating least squares regression algorithm using sums of separable functions and total derivatives to approximate the system of regulation functions from the set of expression data. Considering these as tensor product approximation routines, we develop many new results regarding the best rank-1 tensor approximations. These results help us understand the performance of these regression algorithms. We then consider the analysis of these models to understand the network dynamics and connectivity.
Martin Mohlenkamp, J (Advisor)
Todd Young (Committee Member)
Wei Lin (Committee Member)
Winfried Just (Committee Member)
Sarah Wyatt (Committee Member)
155 p.
Mathematics
machine learning; curse of dimensionality; regulatory networks; multivariate regression; tensor products

Recommended Citations

Citations

  • Botts, R. T. (2010). Recovery and Analysis of Regulatory Networks from Expression Data Using Sums of Separable Functions [Doctoral dissertation, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1275926172

    APA Style (7th edition)

  • Botts, Ryan. Recovery and Analysis of Regulatory Networks from Expression Data Using Sums of Separable Functions. 2010. Ohio University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1275926172.

    MLA Style (8th edition)

  • Botts, Ryan. "Recovery and Analysis of Regulatory Networks from Expression Data Using Sums of Separable Functions." Doctoral dissertation, Ohio University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1275926172

    Chicago Manual of Style (17th edition)

ohiou1275926172
745
© 2010, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.