Paper Info
Title
Predicting disease trait with genomic data: a composite kernel approach.
Abstract
With the advancement of biotechniques, a vast amount of genomic data is generated with no limit. Predicting a disease trait based on these data offers a cost-effective and time-efficient way for early disease screening. Here we proposed a composite kernel partial least squares (CKPLS) regression model for quantitative disease trait prediction focusing on genomic data. It can efficiently capture nonlinear relationships among features compared with linear learning algorithms such as Least Absolute Shrinkage and Selection Operator or ridge regression. We proposed to optimize the kernel parameters and kernel weights with the genetic algorithm (GA). In addition to improved performance for parameter optimization, the proposed GA-CKPLS approach also has better learning capacity and generalization ability compared with single kernel-based KPLS method as well as other nonlinear prediction models such as the support vector regression. Extensive simulation studies demonstrated that GA-CKPLS had better prediction performance than its counterparts under different scenarios. The utility of the method was further demonstrated through two case studies. Our method provides an efficient quantitative platform for disease trait prediction based on increasing volume of omics data.
Year
DOI
Venue
2017
10.1093/bib/bbw043
BRIEFINGS IN BIOINFORMATICS
Keywords
Field
DocType
genetic algorithm,kernel partial least squares,nonlinear prediction,quantitative trait prediction
Kernel partial least squares,Pattern recognition,Biology,Trait,Artificial intelligence,Bioinformatics,Composite kernel,Machine learning,Genetic algorithm,Nonlinear prediction
Journal
Volume
Issue
ISSN
18
4
1467-5463
Citations 
PageRank 
References 
1
0.36
15
Authors
5
Name
Order
Citations
PageRank
Haitao Yang121.08
Shaoyu Li210.70
Hongyan Cao321.75
Chichen Zhang450.76
Yuehua Cui511.04