What question did this study set out to answer?

The aim is to identify transcriptomic changes that predict progression from dengue fever to severe dengue.

May 10, 2026Evolutionary Bioinformatics1 citationsOpen Access

Hybrid Feature Selection-Based Machine Learning and Deep Learning Framework for Biomarker Prediction From RNA-seq Data During Dengue Fever to Severe Dengue Progression

SASrilekha AnumulapuriIndian Institute of Chemical Technology JJJhansi Venkata Nagamani JosyulaIndian Institute of Chemical Technology APAgieshkumar Balakrishna PillaiA P J Abdul Kalam Technological University

Key Points

The aim is to identify transcriptomic changes that predict progression from dengue fever to severe dengue.
RNA-seq datasets from 103 samples (62 SD and 41 DF) were used for analysis.
Differentially expressed genes were identified following normalization using DESeq2.
A hybrid framework using machine learning and deep learning models was applied to classify samples.
55 dysregulated genes were identified, enriched in pathways related to metal ion homeostasis and oxidative stress.
The Transformer-CNN model achieved a test AUC of 0.85 and balanced accuracy of 0.89.
Key discriminative features included ILDR2, TCP1, and SEC14L5.

Abstract

Background: Severe dengue (SD) represents a life-threatening progression of dengue virus infection. Early identification of patients at risk of transitioning from dengue fever (DF) to SD remains a major clinical challenge. Unraveling the transcriptomic changes underlying this progression may aid in developing timely therapeutic interventions. Methods: RNA-seq datasets comprising 103 samples (62 SD and 41 DF) were retrieved from the GEO repository. Following normalization using DESeq2, differentially expressed genes (DEGs) were identified between the 2 disease stages. Functional enrichment analysis was performed to uncover dysregulated biological processes. A hybrid computational framework combining classical machine learning (Logistic Regression, Support Vector Machine, and Random Forest) and deep learning models (Artificial Neural Network, Convolutional Neural Network, and Transformer-based architectures) were applied to classify SD and DF samples. Model performance was evaluated using ROC-AUC and balanced accuracy metrics. Results: Differential expression analysis identified 55 significantly dysregulated genes distinguishing severe dengue from dengue fever. These genes were enriched in pathways related to metal ion homeostasis, platelet signaling, ferroptosis, and oxidative stress. Among multiple machine learning and deep learning models, the Transformer-CNN achieved the best performance (test AUC = 0.85; balanced accuracy = 0.89). SHAP-based interpretation highlighted ILDR2, TCP1, HNRNPUL1, SEC14L5, ATP2C2, LOXL3, ACVRL1, STEAP3 , and ST8SSIA5 as key discriminative features. Integrated network analyses further implicated coordinated regulation of iron metabolism, calcium signaling, and platelet dysfunction in severe dengue. Conclusion: This study integrates RNA-seq and hybrid Artificial Intelligence modeling to identify transcriptomic signatures associated with dengue severity. The study highlights candidate genes and pathways that provide a hypothesis-generating foundation; further increasing the sample size and experimental validation will support early risk stratification in severe dengue.

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