Jackknife model averaging prediction methods for complex phenotypes with gene expression levels by integrating external pathway information
type: Item
instance of: scholarly article
title: Jackknife model averaging prediction methods for complex phenotypes with gene expression levels by integrating external pathway information
zbMATH Open document ID: 1423.92063
DOI: 10.1155/2019/2807470
author: Xinghao Yu
author: Lishun Xiao
author: Ping Zeng
author: Shuiping Huang
published in: Computational \& Mathematical Methods in Medicine
publication date: 2019-07-09T00:00:00Z
review text: Summary: \textit{Motivation}. In the past few years many prediction approaches have been proposed and widely employed in high dimensional genetic data for disease risk evaluation. However, those approaches typically ignore in model fitting the important group structures that naturally exists in genetic data. \textit{Methods}. In the present study, we applied a novel model-averaging approach, called jackknife model averaging prediction (JMAP), for high dimensional genetic risk prediction while incorporating pathway information into the model specification. JMAP selects the optimal weights across candidate models by minimizing a cross validation criterion in a jackknife way. Compared with previous approaches, one of the primary features of JMAP is to allow model weights to vary from 0 to 1 but without the limitation that the summation of weights is equal to one. We evaluated the performance of JMAP using extensive simulation studies and compared it with existing methods. We finally applied JMAP to four real cancer datasets that are publicly available from TCGA. \textit{Results}. The simulations showed that compared with other existing approaches (e.g., gsslasso), JMAP performed best or is among the best methods across a range of scenarios. For example, among 14 out of 16 simulation settings with \(\text{PVE}=0.3\), JMAP has an average of 0.075 higher prediction accuracy compared with gsslasso. We further found that in the simulation, the model weights for the true candidate models have much smaller chances to be zero compared with those for the null candidate models and are substantially greater in magnitude. In the real data application, JMAP also behaves comparably or better compared with the other methods for continuous phenotypes. For example, for the COAD, CRC, and PAAD datasets, the average gains of predictive accuracy of JMAP are 0.019, 0.064, and 0.052 compared with gsslasso. \textit{Conclusion}. The proposed method JMAP is a novel model-averaging approach for…
Mathematics Subject Classification ID: 92C40
Mathematics Subject Classification ID: 62P10
Mathematics Subject Classification ID: 62F40
zbMATH DE Number: 7077756
zbMATH Keywords: jackknife model averaging prediction
zbMATH Keywords: gene expression levels
zbMATH Keywords: complex phenotypes
Wikidata QID: Q64066372
cites work: GWAS Catalog
cites work: LDAK
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describes a project that uses: GWAS Catalog
describes a project that uses: LDAK
describes a project that uses: GeneSrF
describes a project that uses: KEGG
describes a project that uses: clusterProfiler
describes a project that uses: ElemStatLearn
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2019/2807470
OpenAlex ID: W2895781666
DBLP publication ID: journals/cmmm/YuXZH19
description: scientific article; zbMATH DE number 7077756