Authors:
Emma Qumsiyeh
1
;
Miar Yousef
2
and
Malik Yousef
3
;
4
Affiliations:
1
Department of Computer Science and Information Technology, Al-Quds University, Palestine
;
2
Lady Davis Carmel Medical Center, Haifa, Israel
;
3
Department of Information Systems, Zefat Academic College, Zefat, Israel
;
4
Galilee Digital Health Research Center, Zefat Academic College, Zefat, Israel
Keyword(s):
Biological Integrative Approach, Machine Learning, Feature Selection, Grouping, Scoring, Modeling, Robust Rank Aggregation, Rescore, Biomarkers.
Abstract:
The integrating of biological prior knowledge for disease gene associations has shown significant promise in discovering new biomarkers with potential translational applications. GediNET is a recent tool that is considered an integrative approach. In this research paper, we aim to enhance the functionality of GediNET by incorporating ten different machine learning algorithms. A critical element of this study involves utilizing the Robust Rank Aggregation method to aggregate all the ranked lists over the cross-validations, suggesting the final ranked significant list of disease groups. The Robust Rank Aggregation is used to re-score disease groups based on multiple machine learning. Moreover, a comprehensive comparative analysis of these ten machine learning algorithms has revealed insights regarding their intrinsic qualities. This facilitates researchers in determining which algorithm is most effective in the context of disease grouping and classification.