Authors:
Emma Qumsiyeh
1
;
Miray Yazıcı
2
and
Malik Yousef
3
;
4
Affiliations:
1
Department of Information Technology Engineering, Al-Quds University, Palestine
;
2
Department of Bioengineering, Faculty of Engineering, Abdullah Gül University, Kayseri, Turkey
;
3
Department of Information Systems, Zefat Academic College, Zefat, 13206, Israel
;
4
Galilee Digital Health Research Center (GDH), Zefat Academic College, Israel
Keyword(s):
Biological Integrative Approach, Machine Learning, Disease-Disease Association, Grouping, Scoring, Modeling, Cross-Validation, K-means, Heatmap, Breast Cancer, Biomarkers.
Abstract:
The GediNET tool is based on the Grouping, Scoring, Modeling (G-S-M) approach for detecting disease-disease association (DDA). In this study, we have developed the pro version, GediNETPro, that utilizes the Cross-Validation (CV) information to detect patterns of disease groups association by applying clustering approaches, such as K-means, extracted from the groups’ ranks over the CV iterations. Additionally, a cluster score is computed to measure its significance to provide a deep analysis of the output of GediNET, yielding new biological knowledge that GediNET did not detect. Further, GediNETPro utilizes a visualization approach, such as a heatmap, to get novel insights and in-depth analysis of the disease groups clusters revealing the relationship between diseases that might be used for developing effective interventions for diagnosing. We have tested GediNETPro on the Breast cancer dataset downloaded from the TCGA database. Results showed deeper insight into the interaction and c
ollective behavior of the DDA, facilitating the identification and association of potential biomarkers.
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