首页 | 本学科首页   官方微博 | 高级检索  
   检索      


Integration of relational and hierarchical network information for protein function prediction
Authors:Xiaoyu Jiang  Naoki Nariai  Martin Steffen  Simon Kasif  Eric D Kolaczyk
Institution:(1) Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA;(2) Bioinformatics Program, Boston University, Boston, MA 02215, USA;(3) Department of Genetics and Genomics, Boston University, Boston, MA 02118, USA;(4) Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
Abstract:

Background  

In the current climate of high-throughput computational biology, the inference of a protein's function from related measurements, such as protein-protein interaction relations, has become a canonical task. Most existing technologies pursue this task as a classification problem, on a term-by-term basis, for each term in a database, such as the Gene Ontology (GO) database, a popular rigorous vocabulary for biological functions. However, ontology structures are essentially hierarchies, with certain top to bottom annotation rules which protein function predictions should in principle follow. Currently, the most common approach to imposing these hierarchical constraints on network-based classifiers is through the use of transitive closure to predictions.
Keywords:
本文献已被 SpringerLink 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号