Diagnostic biases in translational bioinformatics.

ABSTRACT:

Background

With the surge of translational medicine and computational omics research, complex disease diagnosis is more and more relying on massive omics data-driven molecular signature detection. However, how to detect and prevent possible diagnostic biases in translational bioinformatics remains an unsolved problem despite its importance in the coming era of personalized medicine.

Methods

In this study, we comprehensively investigate the diagnostic bias problem by analyzing benchmark gene array, protein array, RNA-Seq and miRNA-Seq data under the framework of support vector machines for different model selection methods. We further categorize the diagnostic biases into different types by conducting rigorous kernel matrix analysis and provide effective machine learning meth

SUBMITTER: Han H 

PROVIDER: S-EPMC4522082 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature