أطروحات الدكتوراه (Doctoral Dissertations)

Permanent URI for this community

https://dspace.alquds.edu/handle/20.500.12213/9422

Browse

Browsing أطروحات الدكتوراه (Doctoral Dissertations) by Author "Emma Mamdouh Jeries Qumsiyeh"

Now showing 1 - 1 of 1
  • Thumbnail Image
    Item
    Discovering Gene Associations Across Diseases Using a Knowledge-based Machine Learning Approach
    (Al-Quds university, 2024-11-04) Emma Mamdouh Jeries Qumsiyeh; ايما ممدوح جريس قمصية
    Complex diseases such as diabetes, Alzheimer's, and cancer are influenced by a combination of genetic, lifestyle, and environmental factors that do not follow straightforward inheritance patterns. Biological systems are immensely complex and heterogeneous. To resolve the enigmas surrounding these systems, extensive research provides huge amounts of biological data. In this thesis and in our first study, a novel approach called GediNET was developed to integrate prior biological knowledge into disease-associated gene groups. GediNET employs a Grouping, Scoring, and Modeling (G-S-M) approach to identify top-performing gene groups, which are then used to train a machine-learning model. Following the data exploration and preprocessing steps, various classification models were built with 100-fold Monte Carlo Cross-Validation, and the performance of these models was evaluated. By applying Disease-Disease Association (DDA) based machine learning, GediNET uncovered new relationships between diseases, improving diagnosis, prognosis, and treatment approaches. In the second study, GediNETPro, an advanced version of GediNET, was developed. This version utilizes Cross-Validation (CV) information and clustering techniques, such as K-means, to identify patterns of disease group associations. GediNETPro provides visualization tools, like heatmaps and in-depth analysis of disease group clusters, offering insights for developing effective diagnostic interventions. The third study leveraged molecular-level data to develop effective methods for predicting Disease-Disease Associations (DDAs). A statistical technique was developed by employing the G-S-M-P model of GediNETPro to compute semantic similarity metrics between diseases. The semantic approach detects representative diseases within clusters and establishes a semantic relationship between the disease under investigation and other diseases. The studies presented in this thesis contribute to understanding disease complexity, uncovering disease associations, and identifying potential biomarkers and drug targets