Using differential geometric lars algorithm to study the expression profile of a sample of patients with latex-fruit syndrome
Luigi Augugliaro,Angelo Mineo +1 more
TL;DR: The aim of this study is to use the differential geometric generalization of the LARS algorithm to identify candidate genes that may be associated with the pathogenesis of allergy to latex or vegetable.
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Abstract: Natural rubber latex IgE-mediated hypersensitivity is one of the most important health problems in allergy during recent years. The prevalence of individuals allergic to latex shows an associated hypersensitivity to some plant-derived foods, especially freshly consumed fruit. This association of latex allergy and allergy to plant-derived foods is called latex-fruit syndrome. The aim of this study is to use the differential geometric generalization of the LARS algorithm to identify candidate genes that may be associated with the pathogenesis of allergy to latex or vegetable.
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Citations
Genetic basis of the latex-fruit syndrome: Association with HLA-Class II alleles
Carlos Blanco,Florentino Sánchez-García,María José Torres-Galván,Antonio G. Dumpierrez,L. Almeida,Javier Figueroa,N. Ortega,R. Castillo,M. D. Gallego,Teresa Carrillo +9 more
TL;DR: Investigation of patients allergic to latex, searching for association between latex-fruit allergy and HLA class I and II genes, HLA-DR functional groups, and markers IL4-R1 and FcepsilonRI-betaca found that latex-not- Fruit allergy is associated with DQB1 *0202, and with both DRB1 *0701 and *1101 alleles.
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Gender Gap: Factors Affecting Female Students' Retention in an Online Undergraduate IT Program
Kristina Setzekorn,Tina Burton,Colleen M. Farrelly,Susan Shepherd Ferebee +3 more
- 01 Jan 2020
TL;DR: This study aims to understand faculty gender's impacts on female IT student retention in introductory courses in an online university's undergraduate IT program.
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•Journal Article
L1 Regularization Path Algorithm for Generalized Linear Models
TL;DR: An estimation algorithm of coefficient to select variables for L1 regularized generalized linear models to efficiently compute solutions along the entire regularization path using the predictor-corrector method of convex-optimization.
References
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Regularization and variable selection via the elastic net
Hui Zou,Trevor Hastie +1 more
TL;DR: It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Jianqing Fan,Runze Li +1 more
TL;DR: In this article, penalized likelihood approaches are proposed to handle variable selection problems, and it is shown that the newly proposed estimators perform as well as the oracle procedure in variable selection; namely, they work as well if the correct submodel were known.
Generalized Linear Models
John A. Nelder,R. W. M. Wedderburn +1 more
- 01 May 1972
TL;DR: In this paper, the authors used iterative weighted linear regression to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation.
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Least angle regression
Bradley Efron,Trevor Hastie,Iain M. Johnstone,Robert Tibshirani,Hemant Ishwaran,Keith Knight,Jean-Michel Loubes,Jean-Michel Loubes,Pascal Massart,Pascal Massart,David Madigan,David Madigan,Greg Ridgeway,Greg Ridgeway,Saharon Rosset,Saharon Rosset,Ji Zhu,Robert A. Stine,Berwin A. Turlach,Sanford Weisberg +19 more
TL;DR: A publicly available algorithm that requires only the same order of magnitude of computational effort as ordinary least squares applied to the full set of covariates is described.