Background and motivation Classifying diseases like heart problems using gene expression data depends on selecting important genes. Traditional machine learning (ML) often uses simple feature selection (FS) techniques, which can limit accuracy. In our research, we combine deep learning (DL) with gene-focused methods like differential expression analysis (DEA) to improve classification performance significantly. Method We thoroughly and rigorously evaluated ML and DL classifiers using two gene expression datasets (GSE36961 and GSE57345). We tested four hypotheses using feature selection methods such as chi-square, DEA. We applied principal component analysis (PCA) to reduce the number of features. To ensure the reliability of our findings, we applied k-fold cross-validation, hyperparameter tuning, block effect analysis, and assessed data augmentation and generalization. Statistical tests, including paired t -test and Mann–Whitney U test, and Wilcoxon signed-rank test were performed to compare model performances rigorously. Results Our experiments on two gene expression datasets (GSE36961, GSE57345) not only confirmed all four hypotheses (H1, H2, H3, and H4) but also revealed significant performance improvements. For H1, without FS, DL outperformed ML models by a substantial margin. For H2, with FS, DL outperformed ML models by a significant percentage. In H3, ML with FS improved over ML without FS by a considerable margin. For H4, DL with FS outperformed DL without FS by a noticeable percentage. Among FS methods, DEA consistently yielded the best results for both ML and DL, further underlining the significance of our findings. Conclusions Combining DL with biological feature selection, especially DEA, improves gene expression classification and enables gene ranking and biomarker identification. This integrative approach balances modeling power with biological relevance, providing a reproducible framework for robust biomarker-based classification.
Identification of high-risk genes and classification of acute myocardial infarction patients utilizing deep learning in a restricted cohort
Faa, Gavino;Saba, LucaPenultimo
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2026-01-01
Abstract
Background and motivation Classifying diseases like heart problems using gene expression data depends on selecting important genes. Traditional machine learning (ML) often uses simple feature selection (FS) techniques, which can limit accuracy. In our research, we combine deep learning (DL) with gene-focused methods like differential expression analysis (DEA) to improve classification performance significantly. Method We thoroughly and rigorously evaluated ML and DL classifiers using two gene expression datasets (GSE36961 and GSE57345). We tested four hypotheses using feature selection methods such as chi-square, DEA. We applied principal component analysis (PCA) to reduce the number of features. To ensure the reliability of our findings, we applied k-fold cross-validation, hyperparameter tuning, block effect analysis, and assessed data augmentation and generalization. Statistical tests, including paired t -test and Mann–Whitney U test, and Wilcoxon signed-rank test were performed to compare model performances rigorously. Results Our experiments on two gene expression datasets (GSE36961, GSE57345) not only confirmed all four hypotheses (H1, H2, H3, and H4) but also revealed significant performance improvements. For H1, without FS, DL outperformed ML models by a substantial margin. For H2, with FS, DL outperformed ML models by a significant percentage. In H3, ML with FS improved over ML without FS by a considerable margin. For H4, DL with FS outperformed DL without FS by a noticeable percentage. Among FS methods, DEA consistently yielded the best results for both ML and DL, further underlining the significance of our findings. Conclusions Combining DL with biological feature selection, especially DEA, improves gene expression classification and enables gene ranking and biomarker identification. This integrative approach balances modeling power with biological relevance, providing a reproducible framework for robust biomarker-based classification.
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