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Conference Paper: NRProF: Neural response based protein function prediction algorithm

TitleNRProF: Neural response based protein function prediction algorithm
Authors
KeywordsAlgorithms
Artificial intelligence
Genome annotation
Machine learning
Ontology
Issue Date2011
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800515
Citation
The 2011 IEEE International Conference on Systems Biology (ISB), Zhuhai, China, 2-4 September 2011. In Conference Proceedings, 2011, p. 33-40 How to Cite?
AbstractA large amount of proteomic data is being generated due to the advancements in high-throughput genome sequencing. But the rate of functional annotation of these sequences falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOfigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. The lack of annotation coverage of the existing methods advocates novel methods to improve protein function prediction. Here we present a automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. The main idea of this algorithm is to define a distance metric that corresponds to the similarity of the subsequences and reflects how the human brain can distinguish different sequences. Given query protein, we predict the most similar target protein using a two layered neural response algorithm and thereby assigned the GO term of the target protein to the query. Our method predicted and ranked the actual leaf GO term among the top 5 probable GO terms with 87.66% accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The NRProF program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/. © 2011 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/140070
ISBN
References

 

DC FieldValueLanguage
dc.contributor.authorYalamanchili, HKen_HK
dc.contributor.authorWang, Jen_HK
dc.contributor.authorXiao, QWen_HK
dc.date.accessioned2011-09-23T06:06:17Z-
dc.date.available2011-09-23T06:06:17Z-
dc.date.issued2011en_HK
dc.identifier.citationThe 2011 IEEE International Conference on Systems Biology (ISB), Zhuhai, China, 2-4 September 2011. In Conference Proceedings, 2011, p. 33-40en_HK
dc.identifier.isbn978-1-4577-1666-9-
dc.identifier.urihttp://hdl.handle.net/10722/140070-
dc.description.abstractA large amount of proteomic data is being generated due to the advancements in high-throughput genome sequencing. But the rate of functional annotation of these sequences falls far behind. To fill the gap between the number of sequences and their annotations, fast and accurate automated annotation methods are required. Many methods, such as GOblet, GOfigure, and Gotcha, are designed based on the BLAST search. Unfortunately, the sequence coverage of these methods is low as they cannot detect the remote homologues. The lack of annotation coverage of the existing methods advocates novel methods to improve protein function prediction. Here we present a automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. The main idea of this algorithm is to define a distance metric that corresponds to the similarity of the subsequences and reflects how the human brain can distinguish different sequences. Given query protein, we predict the most similar target protein using a two layered neural response algorithm and thereby assigned the GO term of the target protein to the query. Our method predicted and ranked the actual leaf GO term among the top 5 probable GO terms with 87.66% accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The NRProF program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/. © 2011 IEEE.en_HK
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800515-
dc.relation.ispartofProceedings of the IEEE International Conference on Systems Biology, ISB 2011en_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsProceedings of the IEEE International Conference on Systems Biology. Copyright © IEEE.-
dc.rights©2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectAlgorithmsen_HK
dc.subjectArtificial intelligenceen_HK
dc.subjectGenome annotationen_HK
dc.subjectMachine learningen_HK
dc.subjectOntologyen_HK
dc.titleNRProF: Neural response based protein function prediction algorithmen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailWang, J:junwen@hkucc.hku.hken_HK
dc.identifier.authorityWang, J=rp00280en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ISB.2011.6033117en_HK
dc.identifier.scopuseid_2-s2.0-80054858471en_HK
dc.identifier.hkuros192082en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-80054858471&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage33en_HK
dc.identifier.epage40en_HK
dc.description.otherThe 2011 IEEE International Conference on Systems Biology (ISB), Zhuhai, China, 2-4 September 2011. In Conference Proceedings, 2011, p. 33-40-
dc.identifier.scopusauthoridYalamanchili, HK=35182263500en_HK
dc.identifier.scopusauthoridWang, J=8950599500en_HK
dc.identifier.scopusauthoridXiao, QW=53872227700en_HK

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