Disease candidate gene identification and prioritization using protein interaction networks
<p>Abstract</p> <p>Background</p> <p>Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2009-02-01
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| Series: | BMC Bioinformatics |
| Online Access: | http://www.biomedcentral.com/1471-2105/10/73 |
| _version_ | 1811266655130484736 |
|---|---|
| author | Aronow Bruce J Chen Jing Jegga Anil G |
| author_facet | Aronow Bruce J Chen Jing Jegga Anil G |
| author_sort | Aronow Bruce J |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN) analyses.</p> <p>Results</p> <p>For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds"), and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance.</p> <p>Conclusion</p> <p>Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.</p> |
| first_indexed | 2024-04-12T20:47:14Z |
| format | Article |
| id | doaj.art-98a500be43584e9a92204162466b8cf3 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-04-12T20:47:14Z |
| publishDate | 2009-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-98a500be43584e9a92204162466b8cf32022-12-22T03:17:15ZengBMCBMC Bioinformatics1471-21052009-02-011017310.1186/1471-2105-10-73Disease candidate gene identification and prioritization using protein interaction networksAronow Bruce JChen JingJegga Anil G<p>Abstract</p> <p>Background</p> <p>Although most of the current disease candidate gene identification and prioritization methods depend on functional annotations, the coverage of the gene functional annotations is a limiting factor. In the current study, we describe a candidate gene prioritization method that is entirely based on protein-protein interaction network (PPIN) analyses.</p> <p>Results</p> <p>For the first time, extended versions of the PageRank and HITS algorithms, and the K-Step Markov method are applied to prioritize disease candidate genes in a training-test schema. Using a list of known disease-related genes from our earlier study as a training set ("seeds"), and the rest of the known genes as a test list, we perform large-scale cross validation to rank the candidate genes and also evaluate and compare the performance of our approach. Under appropriate settings – for example, a back probability of 0.3 for PageRank with Priors and HITS with Priors, and step size 6 for K-Step Markov method – the three methods achieved a comparable AUC value, suggesting a similar performance.</p> <p>Conclusion</p> <p>Even though network-based methods are generally not as effective as integrated functional annotation-based methods for disease candidate gene prioritization, in a one-to-one comparison, PPIN-based candidate gene prioritization performs better than all other gene features or annotations. Additionally, we demonstrate that methods used for studying both social and Web networks can be successfully used for disease candidate gene prioritization.</p>http://www.biomedcentral.com/1471-2105/10/73 |
| spellingShingle | Aronow Bruce J Chen Jing Jegga Anil G Disease candidate gene identification and prioritization using protein interaction networks BMC Bioinformatics |
| title | Disease candidate gene identification and prioritization using protein interaction networks |
| title_full | Disease candidate gene identification and prioritization using protein interaction networks |
| title_fullStr | Disease candidate gene identification and prioritization using protein interaction networks |
| title_full_unstemmed | Disease candidate gene identification and prioritization using protein interaction networks |
| title_short | Disease candidate gene identification and prioritization using protein interaction networks |
| title_sort | disease candidate gene identification and prioritization using protein interaction networks |
| url | http://www.biomedcentral.com/1471-2105/10/73 |
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