NDLI: The LASSO and Sparse Least Squares Regression Methods for SNP Selection in Predicting Quantitative Traits

Please wait, while we are loading the content...

Identifying Bacterial Virulent Proteins by Fusing a Set of Classifiers Based on Variants of Chou's Pseudo Amino Acid Composition and on Evolutionary Information

Molecular Dynamics Trajectory Compression with a Coarse-Grained Model

Multiscale Binarization of Gene Expression Data for Reconstructing Boolean Networks

Mutation Region Detection for Closely Related Individuals without a Known Pedigree

On Complexity of Protein Structure Alignment Problem under Distance Constraint

On the Elusiveness of Clusters

Optimizing Phylogenetic Networks for Circular Split Systems

Output-Sensitive Algorithms for Finding the Nested Common Intervals of Two General Sequences

Parallelized Evolutionary Learning for Detection of Biclusters in Gene Expression Data

Quantum Gate Circuit Model of Signal Integration in Bacterial Quorum Sensing

Robust Classification Method of Tumor Subtype by Using Correlation Filters

SimBioNeT: A Simulator of Biological Network Topology

Structural SCOP Superfamily Level Classification Using Unsupervised Machine Learning

Subcellular Localization Prediction through Boosting Association Rules

The Impact of Normalization and Phylogenetic Information on Estimating the Distance for Metagenomes

The LASSO and Sparse Least Squares Regression Methods for SNP Selection in Predicting Quantitative Traits

What's new in Transactions [advertisement]

Take the CS Library wherever you go! [advertisement]

Stay Connected to the CSDL

Advertisement - IEEE Computer Society Jobs board

Issue 1

Year : 2011 Volume : 8

Year : 2010 Volume : 7

Year : 2009 Volume : 6

Year : 2008 Volume : 5

Year : 2007 Volume : 4

Year : 2006 Volume : 3

Year : 2005 Volume : 2

Year : 2004 Volume : 1

The LASSO and Sparse Least Squares Regression Methods for SNP Selection in Predicting Quantitative Traits

Content Provider	IEEE Xplore Digital Library
Author	Feng, Z.Z. Xiaojian Yang Subedi, S. McNicholas, P.D.
Copyright Year	2004
Abstract	Recent work concerning quantitative traits of interest has focused on selecting a small subset of single nucleotide polymorphisms (SNPs) from among the SNPs responsible for the phenotypic variation of the trait. When considered as covariates, the large number of variables (SNPs) and their association with those in close proximity pose challenges for variable selection. The features of sparsity and shrinkage of regression coefficients of the least absolute shrinkage and selection operator (LASSO) method appear attractive for SNP selection. Sparse partial least squares (SPLS) is also appealing as it combines the features of sparsity in subset selection and dimension reduction to handle correlations among SNPs. In this paper, we investigate application of the LASSO and SPLS methods for selecting SNPs that predict quantitative traits. We evaluate the performance of both methods with different criteria and under different scenarios using simulation studies. Results indicate that these methods can be effective in selecting SNPs that predict quantitative traits but are limited by some conditions. Both methods perform similarly overall but each exhibit advantages over the other in given situations. Both methods are applied to Canadian Holstein cattle data to compare their performance.
Sponsorship	IEEE Computer Society
Page Count	8
File Size	1190647
Starting Page	629
Ending Page	636
File Format	PDF
ISSN	15455963
Volume Number	9
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Predictive models Biological cells Bioinformatics Correlation Input variables Accuracy statistical computing. regression analysis
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Genetics Biotechnology

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Compressed and Privacy-Sensitive Sparse Regression

Some theoretical results on the Grouped Variables Lasso

Gene Association Networks from Microarray Data Using a Regularized Estimation of Partial Correlation Based on PLS Regression

Linear Regression With a Sparse Parameter Vector

Multinomial Least Angle Regression

A New Approach to Variable Selection Using the TLS Approach

Weighted Least Squares and Least Median Squares estimation for the fuzzy linear regression analysis

New Developments in Sparse PLS Regression

A generalized method for Sparse Partial Least Squares (Dual-SPLS): theory and applications

The LASSO and Sparse Least Squares Regression Methods for SNP Selection in Predicting Quantitative Traits

Similar Documents

Compressed and Privacy-Sensitive Sparse Regression

Some theoretical results on the Grouped Variables Lasso

Gene Association Networks from Microarray Data Using a Regularized Estimation of Partial Correlation Based on PLS Regression

Linear Regression With a Sparse Parameter Vector

Multinomial Least Angle Regression

A New Approach to Variable Selection Using the TLS Approach

Weighted Least Squares and Least Median Squares estimation for the fuzzy linear regression analysis

New Developments in Sparse PLS Regression

A generalized method for Sparse Partial Least Squares (Dual-SPLS): theory and applications

The LASSO and Sparse Least Squares Regression Methods for SNP Selection in Predicting Quantitative Traits