Paper Info
Title
Enhancing the Lasso Approach for Developing a Survival Prediction Model Based on Gene Expression Data
Abstract
In the past decade, researchers in oncology have sought to develop survival prediction models using gene expression data. The least absolute shrinkage and selection operator (lasso) has been widely used to select genes that truly correlated with a patient's survival. The lasso selects genes for prediction by shrinking a large number of coefficients of the candidate genes towards zero based on a tuning parameter that is often determined by a cross-validation (CV). However, this method can pass over (or fail to identify) true positive genes (i.e., it identifies false negatives) in certain instances, because the lasso tends to favor the development of a simple prediction model. Here, we attempt to monitor the identification of false negatives by developing a method for estimating the number of true positive (TP) genes for a series of values of a tuning parameter that assumes a mixture distribution for the lasso estimates. Using our developed method, we performed a simulation study to examine its precision in estimating the number of TP genes. Additionally, we applied our method to a real gene expression dataset and found that it was able to identify genes correlated with survival that a CV method was unable to detect.
Year
DOI
Venue
2015
10.1155/2015/259474
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
Field
DocType
Volume
Mixture distribution,Regression analysis,Computer science,Lasso (statistics),Artificial intelligence,Predictive modelling,Computational biology,Candidate gene,Proportional hazards model,Statistics,False Negative Reactions,Gene expression profiling,Machine learning
Journal
2015
ISSN
Citations 
PageRank 
1748-670X
2
0.49
References 
Authors
3
3
Name
Order
Citations
PageRank
Shuhei Kaneko120.49
Akihiro Hirakawa220.49
Chikuma Hamada321.17