Advances in Computational Approaches for Disease Prediction: A Comprehensive Survey of Candidate Gene Identification Using Protein-Protein Interaction (PPI) Networks and Multi-Omics Integration
Keywords:
Candidate gene prediction, protein-protein interaction (PPI) networks, disease prediction, network-based algorithms, machine learning, multi-omics integration, gene-disease association, computational biology, gene analysis, bioinformaticsAbstract
Disease prediction plays a crucial role in understanding the underlying genetic factors responsible for various conditions and improving early diagnosis and treatment. Computational methods have become essential tools in identifying genes linked to diseases, aiding in the discovery of new therapeutic targets. One of the key areas of research in this field is the use of protein-protein interaction (PPI) networks and gene analysis to predict candidate genes involved in disease processes. This research focuses on the prediction of candidate genes using PPI networks, examining various computational approaches to identify genes potentially involved in specific biological processes, diseases, or traits. By leveraging the structural and functional insights provided by PPI networks, these methods infer gene involvement based on their interactions with known disease-associated proteins. Techniques range from network-based algorithms, which analyze the topological properties of PPI networks, to machine learning models that predict gene-disease associations using complex data patterns. Additionally, integrative approaches combine PPI networks with multi-omics data, such as genomics, transcriptomics, and epigenomics, to enhance the biological relevance of predictions. This survey highlights the strengths and limitations of each method, offering a comprehensive overview of the evolving landscape of candidate gene prediction.
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