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Functional annotation prediction: All for one and one for all
Author(s) -
Sasson Ori,
Kaplan Noam,
Linial Michal
Publication year - 2006
Publication title -
protein science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.353
H-Index - 175
eISSN - 1469-896X
pISSN - 0961-8368
DOI - 10.1110/ps.062185706
Subject(s) - annotation , computer science , hierarchy , uniprot , protein function prediction , function (biology) , data mining , similarity (geometry) , artificial intelligence , computational biology , protein function , machine learning , biology , image (mathematics) , economics , market economy , gene , biochemistry , evolutionary biology
In an era of rapid genome sequencing and high‐throughput technology, automatic function prediction for a novel sequence is of utter importance in bioinformatics. While automatic annotation methods based on local alignment searches can be simple and straightforward, they suffer from several drawbacks, including relatively low sensitivity and assignment of incorrect annotations that are not associated with the region of similarity. ProtoNet is a hierarchical organization of the protein sequences in the UniProt database. Although the hierarchy is constructed in an unsupervised automatic manner, it has been shown to be coherent with several biological data sources. We extend the ProtoNet system in order to assign functional annotations automatically. By leveraging on the scaffold of the hierarchical classification, the method is able to overcome some frequent annotation pitfalls.