A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis.

ABSTRACT:

Background

DNA microarrays provide informative data for transcriptional profiling and identifying gene expression signatures to help prevent progression of latent tuberculosis infection (LTBI) to active disease. However, constructing a prognostic model for distinguishing LTBI from active tuberculosis (ATB) is very challenging due to the noisy nature of data and lack of a generally stable analysis approach.

Methods

In the present study, we proposed an accurate predictive model with the help of data fusion at the decision level. In this regard, results of filter feature selection and wrapper feature selection techniques were combined with multiple-criteria decision-making (MCDM) methods to select 10 genes from six microarray datasets that can be the most discriminative genes f

SUBMITTER: Ayalvari S 

PROVIDER: S-EPMC11523813 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature