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

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.

Estimating a Ranked List of Human Genetic Diseases by Associating Phenotype-Gene with Gene-Disease Bipartite Graphs

“…Finally, candidate diseases are ranked according to the approximate weight in order to define the most probable diseases for a given set of clinical phenotypes. This work is an extended version of our previous work [Ullah et al 2013].…”

Network Analysis and Experimental Validation of the Five Shu Points Model of Traditional Oriental Medicine

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The Challenge of Analyzing the Results of Next-Generation Sequencing in Children

“…HPO currently contains ∼11 000 terms and >115 000 annotations to hereditary diseases. 20 Mode of inheritance and a comprehensive phenotype can then be used to review the published literature and to search relevant databases. Search engines include tools such as Google and PubMed; more specialized searching can use tools such as Orphanet, DECIPHER, and OMIM (Online Mendelian Inheritance in Man).…”

“…Search engines include tools such as Google and PubMed; more specialized searching can use tools such as Orphanet, DECIPHER, and OMIM (Online Mendelian Inheritance in Man). 20 Bioinformatic tools such as Phenomizer can help develop a differential diagnosis using HPO to identify candidate diseases/ disease genes that best explain a patient's set of clinical features. 20,21 These tools allow some classification of the patient's genomic variants.…”

“…20 Bioinformatic tools such as Phenomizer can help develop a differential diagnosis using HPO to identify candidate diseases/ disease genes that best explain a patient's set of clinical features. 20,21 These tools allow some classification of the patient's genomic variants. NGS may lead to the discovery of a known pathogenic variant, a novel Clinical validity is a complicated and challenging aspect of NGS.…”