TY  - JOUR
AU  - Elmakki, Shimaa M.
AU  - Hashem, Esraa M.
AU  - Hadhoud, Marwa M. A.
AU  - Ghoneim, Vidan F.
PY  - 2026
DA  - 2026/04/07
TI  - Improved ALS Biomarker Discovery with SMOTE-Augmented Gene Expression Data
JO  - Journal of Computational Intelligence in Biomedicine
T2  - Journal of Computational Intelligence in Biomedicine
JF  - Journal of Computational Intelligence in Biomedicine
VL  - 1
IS  - 1
SP  - 1
EP  - 9
DO  - 10.62762/JCIB.2025.140919
UR  - https://www.icck.org/article/abs/JCIB.2025.140919
KW  - amyotrophic lateral sclerosis
KW  - synthetic minority over-sampling technique
KW  - transcriptome
KW  - biomarker
AB  - The early identification of Amyotrophic Lateral Sclerosis (ALS), a progressive neurological disease, using blood-based transcriptome biomarker is gaining attention. The classification of ALS from blood transcriptomic data remains challenging due to class imbalance and high dimensionality. This extension of a previous study that utilized machine learning on the microarray dataset includes a synthetic data augmentation method employing the Synthetic Minority Over-sampling Technique (SMOTE) to improve classification accuracy. Following the use of Fisher Score, t-test, PCA, and Ant Colony Optimization for feature selection, SMOTE was employed to produce synthetic ALS samples and to imbalance the class distribution. Support Vector Machines, ensemble techniques, and k-Nearest Neighbors were used to assess the classifier's performance. The accuracy of all models improved, according to the results, with k-NN rising from 77.5% to 82% and SVM rising from 91.3% to 93%. Furthermore, a number of physiologically significant genes, such as MMP9 and SELL, appeared more noticeable after augmentation and matched known immune-related indicators in ALS. The augmentation technique improves both the predictive performance, and the biological validity of the biomarkers identified. These findings demonstrate the utility of SMOTE in enhancing transcriptomic classifiers.
SN  - request pending
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Elmakki2026Improved,
  author = {Shimaa M. Elmakki and Esraa M. Hashem and Marwa M. A. Hadhoud and Vidan F. Ghoneim},
  title = {Improved ALS Biomarker Discovery with SMOTE-Augmented Gene Expression Data},
  journal = {Journal of Computational Intelligence in Biomedicine},
  year = {2026},
  volume = {1},
  number = {1},
  pages = {1-9},
  doi = {10.62762/JCIB.2025.140919},
  url = {https://www.icck.org/article/abs/JCIB.2025.140919},
  abstract = {The early identification of Amyotrophic Lateral Sclerosis (ALS), a progressive neurological disease, using blood-based transcriptome biomarker is gaining attention. The classification of ALS from blood transcriptomic data remains challenging due to class imbalance and high dimensionality. This extension of a previous study that utilized machine learning on the microarray dataset includes a synthetic data augmentation method employing the Synthetic Minority Over-sampling Technique (SMOTE) to improve classification accuracy. Following the use of Fisher Score, t-test, PCA, and Ant Colony Optimization for feature selection, SMOTE was employed to produce synthetic ALS samples and to imbalance the class distribution. Support Vector Machines, ensemble techniques, and k-Nearest Neighbors were used to assess the classifier's performance. The accuracy of all models improved, according to the results, with k-NN rising from 77.5\% to 82\% and SVM rising from 91.3\% to 93\%. Furthermore, a number of physiologically significant genes, such as MMP9 and SELL, appeared more noticeable after augmentation and matched known immune-related indicators in ALS. The augmentation technique improves both the predictive performance, and the biological validity of the biomarkers identified. These findings demonstrate the utility of SMOTE in enhancing transcriptomic classifiers.},
  keywords = {amyotrophic lateral sclerosis, synthetic minority over-sampling technique, transcriptome, biomarker},
  issn = {request pending},
  publisher = {Institute of Central Computation and Knowledge}
}