Nonlinear Measures of Correlation and Dimensionality Reduction with Application to Protein Motion
Links to Fileshttps://userpages.umbc.edu/~gobbert/papers/REU2014Team1.pdf
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Type of Work7 pages
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SubjectsHigh Performance Computing Facility (HPCF)
motional correlations that can be analyzed using different statistical methods
polynomial regression and leave-one-out cross-validation
model relationships in data obtained from different sites in the protein
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.