Dr Md Zia Ullah M.Ullah@napier.ac.uk
Lecturer
With vast amounts of medical knowledge available on the Internet, it is becoming increasingly practical to help doctors in clinical diagnostics by suggesting plausible diseases predicted by applying data and text mining technologies. Recently, Genome-Wide Association Studies (GWAS) have proved useful as a method for exploring phenotypic associations with diseases. However, since genetic diseases are difficult to diagnose because of their low prevalence, large number, and broad diversity of symptoms, genetic disease patients are often misdiagnosed or experience long diagnostic delays. In this article, we propose a method for ranking genetic diseases for a set of clinical phenotypes. In this regard, we associate a phenotype-gene bipartite graph (PGBG) with a gene-disease bipartite graph (GDBG) by producing a phenotype-disease bipartite graph (PDBG), and we estimate the candidate weights of diseases. In our approach, all paths from a phenotype to a disease are explored by considering causative genes to assign a weight based on path frequency, and the phenotype is linked to the disease in a new PDBG. We introduce the Bidirectionally induced Importance Weight (BIW) prediction method to PDBG for approximating the weights of the edges of diseases with phenotypes by considering link information from both sides of the bipartite graph. The performance of our system is compared to that of other known related systems by estimating Normalized Discounted Cumulative Gain (NDCG), Mean Average Precision (MAP), and Kendall’s tau metrics. Further experiments are conducted with well-known TF · IDF, BM25, and Jenson-Shannon divergence as baselines. The result shows that our proposed method outperforms the known related tool Phenomizer in terms of NDCG@10, NDCG@20, MAP@10, and MAP@20; however, it performs worse than Phenomizer in terms of Kendall’s tau-b metric at the top-10 ranks. It also turns out that our proposed method has overall better performance than the baseline methods.
Ullah, M. Z., Aono, M., & Seddiqui, M. H. (2015). Estimating a Ranked List of Human Genetic Diseases by Associating Phenotype-Gene with Gene-Disease Bipartite Graphs. ACM transactions on intelligent systems and technology, 6(4), Article 56. https://doi.org/10.1145/2700487
Journal Article Type | Article |
---|---|
Acceptance Date | Dec 1, 2014 |
Online Publication Date | Jul 4, 2015 |
Publication Date | 2015-08 |
Deposit Date | Mar 13, 2023 |
Journal | ACM Transactions on Intelligent Systems and Technology |
Print ISSN | 2157-6904 |
Electronic ISSN | 2157-6912 |
Publisher | Association for Computing Machinery (ACM) |
Peer Reviewed | Peer Reviewed |
Volume | 6 |
Issue | 4 |
Article Number | 56 |
DOI | https://doi.org/10.1145/2700487 |
Public URL | http://researchrepository.napier.ac.uk/Output/3010949 |
Instruments and Tools to Identify Radical Textual Content
(2022)
Journal Article
Can we predict QPP? An approach based on multivariate outliers
(2024)
Presentation / Conference Contribution
Prediction and Visual Intelligence for Security Information: The PREVISION H2020 Project
(2020)
Presentation / Conference Contribution
InnEO'Space PhD: Preparing Young Researchers for a successful career on Earth Observation applications
(2022)
Presentation / Conference Contribution
About Edinburgh Napier Research Repository
Administrator e-mail: repository@napier.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search