Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.12188/22949
DC FieldValueLanguage
dc.contributor.authorMirceva, Georginaen_US
dc.contributor.authorKulakov, Andreaen_US
dc.date.accessioned2022-09-07T08:45:38Z-
dc.date.available2022-09-07T08:45:38Z-
dc.date.issued2013-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/22949-
dc.description.abstractProtein molecules play essential roles in the living organisms. The knowledge about their functions is very important in order to design new drugs that could be used to control various processes in the organisms. The determination of the protein functions could be performed by detecting the binding sites where interactions between proteins occur. In this paper we focus on predicting the protein binding sites. First, several characteristics of the amino acid residues are extracted. Then, prediction methods are induced. In this research paper we consider several classification methods for inducing models. In order to enhance the predictions, we use ensembles, which combine several classification models. The results show that using ensembles, the prediction power is increased.en_US
dc.subjectProtein function, protein interaction, protein binding site, BIND database, ensemblesen_US
dc.titleProtein Binding Sites Prediction Using Ensemblesen_US
dc.typeProceedingsen_US
dc.relation.conferenceICT Innovations 2013en_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
crisitem.author.deptFaculty of Computer Science and Engineering-
Appears in Collections:Faculty of Computer Science and Engineering: Conference papers
Files in This Item:
File Description SizeFormat 
10.1.1.404.257.pdf740.03 kBAdobe PDFView/Open
Show simple item record

Page view(s)

27
checked on May 19, 2024

Download(s)

4
checked on May 19, 2024

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.