Estimating a ranked list of human hereditary diseases for clinical phenotypes by using weighted bipartite network

Ullah, Md Zia; Aono, Masaki; Seddiqui, Md Hanif

Abstract

With the availability of the huge medical knowledge data on the Internet such as the human disease network, protein-protein interaction (PPI) network, and phenotypegene, gene-disease bipartite networks, it becomes practical to help doctors by suggesting plausible hereditary diseases for a set of clinical phenotypes. However, identifying candidate diseases that best explain a set of clinical phenotypes by considering various heterogeneous networks is still a challenging task. In this paper, we propose a new method for estimating a ranked list of plausible diseases by associating phenotypegene with gene-disease bipartite networks. Our approach is to count the frequency of all the paths from a phenotype to a disease through their associated causative genes, and link the phenotype to the disease with path frequency in a new phenotype-disease bipartite (PDB) network. After that, we generate the candidate weights for the edges of phenotypes with diseases in PDB network. We evaluate our proposed method in terms of Normalized Discounted Cumulative Gain (NDCG), and demonstrate that we outperform the previously known disease ranking method called Phenomizer.

Citation

Ullah, M. Z., Aono, M., & Seddiqui, M. H. (2013). Estimating a ranked list of human hereditary diseases for clinical phenotypes by using weighted bipartite network. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (3475-3478). https://doi.org/10.1109/EMBC.2013.6610290

• Conference Name: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
• Conference Location: Osaka, Japan
• Start Date: Jul 3, 2013
• End Date: Jul 7, 2013
• Online Publication Date: Sep 26, 2013
• Publication Date: 2013
• Deposit Date: Mar 13, 2023
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 3475-3478
• Series ISSN: 1094-687X
• Book Title: 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
• DOI: https://doi.org/10.1109/EMBC.2013.6610290
• Keywords: Algorithms, Computational Biology, Genetic Diseases, Inborn, Humans, Models, Biological, Phenotype