Nonlinear Measures of Correlation and Dimensionality Reduction with Application to Protein Motion
| dc.contributor.author | Hong, Nancy | |
| dc.contributor.author | Jasien, Emily | |
| dc.contributor.author | Pagan, Christopher | |
| dc.contributor.author | Xie, Daniel | |
| dc.contributor.author | Coulibaly, Zana | |
| dc.contributor.author | Adragni, Kofi P. | |
| dc.contributor.author | Thorpe, Ian F. | |
| dc.date.accessioned | 2018-09-25T19:43:03Z | |
| dc.date.available | 2018-09-25T19:43:03Z | |
| dc.date.issued | 2014 | |
| dc.description.abstract | The study of allostery, a regulatory process that occurs in complex macromolecules such as proteins, is of particular interest as it has a key role in determining the function of these macromolecules. Allostery produces motional correlations that can be analyzed using different statistical methods. We implement a program in the statistical programming language R that uses polynomial regression and leave-one-out cross-validation to model relationships in data obtained from different sites in the protein, using the square root of the coefficient of determination to detect both linear and non-linear trends. The performance of the program will be studied on a simulated data set with linear and non-linear relationships and the effectiveness of the implemented methods as it relates to this problem will be assessed. | en_US |
| dc.description.sponsorship | These results were obtained as part of the REU Site: Interdisciplinary Program in High Performance Computing (www.umbc.edu/hpcreu) in the Department of Mathematics and Statistics at the University of Maryland, Baltimore County (UMBC) in Summer 2014. This program is funded jointly by the National Science Foundation and the National Security Agency (NSF grant no. DMS{1156976), with additional support from UMBC, the Department of Mathematics and Statistics, the Center for Interdisciplinary Research and Consulting (CIRC), and the UMBC High Performance Computing Facility (HPCF). HPCF is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS{0821258 and CNS{1228778) and the SCREMS program (grant no. DMS{0821311), with additional substantial support from UMBC. Co-author Christopher Pagan was supported, in part, by the UMBC National Security Agency (NSA) Scholars Program through a contract with the NSA. Graduate assistant Zana Coulibaly was supported during Summer 2014 by UMBC. | en_US |
| dc.description.uri | https://userpages.umbc.edu/~gobbert/papers/REU2014Team1.pdf | en_US |
| dc.format.extent | 7 pages | en_US |
| dc.genre | technical report | en_US |
| dc.identifier | doi:10.13016/M2416T337 | |
| dc.identifier.uri | http://hdl.handle.net/11603/11385 | |
| dc.language.iso | en_US | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Mathematics Department Collection | |
| dc.relation.ispartof | UMBC Faculty Collection | |
| dc.relation.ispartof | UMBC Student Collection | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department | |
| dc.relation.ispartof | UMBC Chemistry & Biochemistry Department | |
| dc.relation.ispartofseries | HPCF Technical Report;HPCF-2014-11 | |
| dc.rights | This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author. | |
| dc.subject | UMBC High Performance Computing Facility (HPCF) | en_US |
| dc.subject | allostery | |
| dc.subject | complex macromolecules | |
| dc.subject | motional correlations that can be analyzed using different statistical methods | |
| dc.subject | polynomial regression and leave-one-out cross-validation | |
| dc.subject | model relationships in data obtained from different sites in the protein | |
| dc.title | Nonlinear Measures of Correlation and Dimensionality Reduction with Application to Protein Motion | en_US |
| dc.type | Text | en_US |