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Quantitative Biology > Quantitative Methods

Title: Ridge Regression Estimated Linear Probability Model Predictions of N-glycosylation in Proteins with Structural and Sequence Dat

Abstract: Absent experimental evidence, a robust methodology to predict the likelihood of N-glycosylation in human proteins is essential for guiding experimental work. Based on the distribution of amino acids in the neighborhood of the NxS/T sequon (N-site); the structural attributes of the N-site that include Accessible Surface Area, secondary structural elements, main-chain phi-psi, turn types; the relative location of the N-site in the primary sequence; and the nature of the glycan bound, the ridge regression estimated linear probability model is used to predict this likelihood. This model yields a Kolmogorov-Smirnov (Gini coefficient) statistic value of about 74% (89%), which is reasonable.
Comments: 20 pages
Subjects: Quantitative Methods (q-bio.QM)
MSC classes: 62J05, 62J07
Cite as: arXiv:1803.06002 [q-bio.QM]
  (or arXiv:1803.06002v1 [q-bio.QM] for this version)

Submission history

From: Rajaram Gana [view email]
[v1] Thu, 15 Mar 2018 20:56:11 GMT (840kb)