Abstract
In exploratory association studies of genes with certain diseases, a single or a small number of genes (features) related with the diseases are selected 1 among many thousands investigated. We investigate the statistical bias and variance of simple yet common (correlation and mutual information based) feature selection algorithms using well-known cross-validation methods (leave-one-out and k-fold) on a gene finding study for hypertension prediction. Our findings show that selected genes are different for different methods and different cross-validation runs for both single gene selection and gene subset selection.
Keywords
Cross-validation
Selection (genetic algorithm)
Feature selection
Gene
Variance (accounting)
Computational biology
Gene selection
Computer science
Correlation
Selection bias
Statistics
Data mining
Artificial intelligence
Biology
Genetics
Mathematics
Gene expression
Subject Areas
Gene expression and cancer classification
·Molecular Biology ·Life Sciences
Bioinformatics and Genomic Networks
·Molecular Biology ·Life Sciences
Genetic Associations and Epidemiology
·Genetics ·Life Sciences
Citations by Year
2014: 1
2016: 1
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