A Hypergraph powered approach to Phenotype-driven Gene Prioritization and Rare Disease Prediction.

In the time of advancing medical technology, there is a critical issue concerning the misdiagnosis of diseases. The aim of this research is to significantly reduce the occurrence of misdiagnoses in medical practice by leveraging hypergraphs and genomic data to improve diagnostic accuracy. We have designed and implemented a sophisticated computational framework for phenotype-driven disease prediction that leverages hypergraphs and genomic data to enhance the accuracy of disease identification, leading to more precise and timely treatments for patients. The study employed robust ranking algorithms, on a sample of 2130 diseases, 4655 genes and 9541 phenotypes collected from reliable sources of Orphanet and Human Phenotype Ontology (HPO) database to achieve highly favorable outcomes. The proposed method outperforms existing state-of-the-art tools such as Phenomizer and GCN, in terms of both prediction accuracy and processing speed. Notably, it captures 50% of causal genes within the top 10 predictions and 85% within the top 100 predictions and the algorithm maintains a high accuracy rate of 98.09% for the top-ranked gene. In conclusion, our study demonstrated the effectiveness of robust ranking algorithms and hypergraph framework in achieving accurate and reliable results for disease diagnosis. While the study provides valuable insights, it is important to note its limitations, such as the sample size and scope of diseases considered. Future research could explore the integration of additional data sources and refinement of algorithms to further enhance diagnostic capabilities. Overall, this study underscores the potential of algorithmic hypergraph based approaches in advancing medical diagnostics and improving healthcare delivery.