ArticleBMC systems biology2017
DIGNiFI: Discovering causative genes for orphan diseases using protein-protein interaction networks.
Article in BMC systems biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Bioinformatics Prediction for Network-Based Integrative Multi-Omics Expression Data Analysis in Hirschsprung Disease.Biomolecules · 2024Article
- Resources and tools for rare disease variant interpretation.Frontiers in molecular biosciences · 2023Review
- New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches.International journal of molecular sciences · 2022Review
- Review
- Selected research articles from the 2016 International Workshop on Computational Network Biology: Modeling, Analysis, and Control (CNB-MAC).BMC bioinformatics · 2017Article
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundAn orphan disease is any disease that affects a small percentage of the population. Orphan diseases are a great burden to patients and society, and most of them are genetic in origin. Unfortunately, our current understanding of the genes responsible for inherited orphan diseases is still quite limited. Developing effective computational algorithms to discover disease-causing genes would help unveil disease mechanisms and may enable better diagnosis and treatment.
resultsWe have developed a novel method, named as DIGNiFI (Disease causIng GeNe FInder), which uses Protein-Protein Interaction (PPI) network-based features to discover and rank candidate disease-causing genes. Specifically, our approach computes topologically similar genes by taking into account both local and global connected paths in PPI networks via Direct Neighbors and Local Random Walks, respectively. Furthermore, since genes with similar phenotypes tend to be functionally related, we have integrated PPI data with gene ontology (GO) annotations and protein complex data to further improve the performance of this approach. Results of 128 orphan diseases with 1184 known disease genes collected from the Orphanet show that our proposed methods outperform existing state-of-the-art methods for discovering candidate disease-causing genes. We also show that further performance improvement can be achieved when enriching the human-curated PPI network data with text-mined interactions from the biomedical literature. Finally, we demonstrate the utility of our approach by applying our method to identifying novel candidate genes for a set of four inherited retinal dystrophies. In this study, we found the top predictions for these retinal dystrophies consistent with literature reports and online databases of other retinal dystrophies.
conclusionsOur method successfully prioritizes orphan-disease-causative genes. This method has great potential to benefit the field of orphan disease research, where resources are scarce and greatly needed.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.