Abstract
Gene-disease associations are inferred on the basis of simi- larities between the proteins encoded by genes. Biological relationships used to define similarities range from interacting proteins, proteins that participate in pathways and protein expression profiles. Though graph ker- nel methods have become a prominent approach for association prediction, most solutions are based on a notion of information diffiusion that does not capture the specificity of different network parts. Here we propose a graph kernel method that explicitly models the configuration of each gene's con- text. An empirical evaluation on several biological databases shows that our proposal is competitive w.r.t. state-of-the-art kernel approaches.
| Original language | English |
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| Title of host publication | ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
| Publisher | i6doc.com publication |
| Pages | 257-262 |
| Number of pages | 6 |
| ISBN (Electronic) | 9782875870391 |
| State | Published - 2017 |
| Externally published | Yes |
| Event | 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017 - Bruges, Belgium Duration: 26 Apr 2017 → 28 Apr 2017 |
Publication series
| Name | ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
|---|
Conference
| Conference | 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2017 |
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| Country/Territory | Belgium |
| City | Bruges |
| Period | 26/04/17 → 28/04/17 |
Bibliographical note
Publisher Copyright:© ESANN 2017 - Proceedings, 25th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. All rights reserved.
ASJC Scopus subject areas
- Artificial Intelligence
- Information Systems