ReScore Disease Groups Based on Multiple Machine Learnings Utilizing the Grouping-Scoring-Modeling Approach

Emma Qumsiyeh, Miar Yousef, Malik Yousef, Malik Yousef

2024

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.

Paper Citation

in Harvard Style

Qumsiyeh E., Yousef M. and Yousef M. (2024). ReScore Disease Groups Based on Multiple Machine Learnings Utilizing the Grouping-Scoring-Modeling Approach. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-688-0, SciTePress, pages 446-453. DOI: 10.5220/0012379400003657

in Bibtex Style

@conference{bioinformatics24,
author={Emma Qumsiyeh and Miar Yousef and Malik Yousef},
title={ReScore Disease Groups Based on Multiple Machine Learnings Utilizing the Grouping-Scoring-Modeling Approach},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2024},
pages={446-453},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012379400003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - ReScore Disease Groups Based on Multiple Machine Learnings Utilizing the Grouping-Scoring-Modeling Approach
SN - 978-989-758-688-0
AU - Qumsiyeh E.
AU - Yousef M.
AU - Yousef M.
PY - 2024
SP - 446
EP - 453
DO - 10.5220/0012379400003657
PB - SciTePress