Name
Abstract | ||
|---|---|---|
Motivation Combining disease relationships across multiple biological levels could aid our understanding of common processes taking place in disease, potentially indicating opportunities for drug sharing. Here, we propose a similarity fusion approach which accounts for differences in information content between different data types, allowing combination of each data type in a balanced manner. Results We apply this method to six different types of biological data (ontological, phenotypic, literature co-occurrence, genetic association, gene expression and drug indication data) for 84 diseases to create a disease map': a network of diseases connected at one or more biological levels. As well as reconstructing known disease relationships, 15% of links in the disease map are novel links spanning traditional ontological classes, such as between psoriasis and inflammatory bowel disease. 62% of links in the disease map represent drug-sharing relationships, illustrating the relevance of the similarity fusion approach to the identification of potential therapeutic relationships. Availability and implementation Freely available under the MIT license at https://github.com/e-oerton/disease-similarity-fusion Supplementary information Supplementary data are available at Bioinformatics online. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1093/bioinformatics/bty754 | BIOINFORMATICS |
Field | DocType | Volume |
Biological data,Data mining,Ontology,Disease,Computer science,Genetic association,Data type,Computational biology | Journal | 35 |
Issue | ISSN | Citations |
7 | 1367-4803 | 0 |
PageRank | References | Authors |
0.34 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Erin Oerton | 1 | 0 | 0.34 |
| Ian N Roberts | 2 | 19 | 3.88 |
| Patrick Lewis | 3 | 8 | 4.26 |
| Tim Guilliams | 4 | 0 | 0.34 |
| Andreas Bender | 5 | 685 | 61.10 |