Article
Predicting disease trait with genomic data: a composite kernel approach.
Briefings in bioinformatics - 1 Jul 2017
Yang Haitao, Li Shaoyu, Cao Hongyan, Zhang Chichen, Cui Yuehua
Abstract excerpt
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...
Read the complete abstract on PubMedTopics
Share this publication in a Topic to start or enrich a Post.