ArticleBMC medical informatics and decision making2024
A modified multiple-criteria decision-making approach based on a protein-protein interaction network to diagnose latent tuberculosis.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Predicting macrosomia and low birth weight with interpretable machine learning.BMC pregnancy and childbirth · 2026Article
- Utilises Machine Learning Techniques to Deeply Analyse the Role of Lysosome-Dependent Cell Death in Endometrial Cancer and Its Interactions With the Tumour Microenvironment.Journal of cellular and molecular medicine · 2025Article
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3 authors.
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Abstract
backgroundDNA 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.
methodsIn 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 for diagnosing tuberculosis cases. As the main contribution of this study, the final ranking function was constructed by combining protein-protein interaction (PPI) network with an MCDM method (called Decision-making Trial and Evaluation Laboratory or DEMATEL) to improve the feature ranking approach.
resultsBy applying data fusion at the decision level on the 10 introduced genes in terms of fusion of classifiers of random forests (RF) and k-nearest neighbors (KNN) regarding Yager's theory, the proposed algorithm reached a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. Finally, with the help of cumulative clustering, the genes involved in the diagnosis of latent and activated tuberculosis have been introduced.
conclusionsThe combination of MCDM methods and PPI networks can significantly improve the diagnosis different states of tuberculosis. CLINICAL TRIAL NUMBER: Not applicable.
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