Abstract: Most researchers work on solving the important issue of identifying biomarkers linked to a certain disease, like cancer, in order to assist in the disease’s diagnosis and treatment. Several research have recently suggested several methods for identifying genes linked to disease. A handful of these methods were created specifically for CRC gene prediction, though. This research presents a novel prediction technique to determine new biomarkers related to CRC that can assist in the diagnosing process. First, we preprocessed four Microarray datasets (GSE4107, GSE8671, GSE9348 and GSE32323) using RMA (Robust Multi-Array Average) method to remove local artifacts and normalize the values. Second, we used the chi-squared test for feature selection to identify some significant features from datasets. Finally, the features were fed to XGBoost (eXtreme Gradient Boosting) to diagnose various test scenarios. The proposed model achieves a high mean accuracy rate and low standard deviation. When compared to other systems, the experiment findings show promise. The predicted biomarkers are validated through a review of the literature.

Mohamed Ashraf, M. M. El-Gayar and Eman Eldaydamony, “Novel Biomarkers for Colorectal Cancer Prediction” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151025

@article{Ashraf2024,
title = {Novel Biomarkers for Colorectal Cancer Prediction},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151025},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151025},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Mohamed Ashraf and M. M. El-Gayar and Eman Eldaydamony}
}