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Conference Paper: A novel neural response algorithm for protein function prediction

TitleA novel neural response algorithm for protein function prediction
Authors
Issue Date2012
PublisherBioMed Central Ltd.. The Journal's web site is located at http://www.biomedcentral.com/bmcsystbiol/
Citation
The 5th IEEE International Conference on Computational Systems Biology (ISB 2011), Zhuhai, China. 2-4 September 2011. In BMC Systems Biology, 2012, v. 6 n. suppl. 1, article no. S19 How to Cite?
AbstractBACKGROUND: Large amounts of data are being generated by high-throughput genome sequencing methods. But the rate of the experimental functional characterization 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. Adding to this, the lack of annotation specificity advocates the need to improve automated protein function prediction. RESULTS: We designed a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. Firstly, we predict the most similar target protein for a given query protein and thereby assign its GO term to the query sequence. When assessed on test set, our method ranked the actual leaf GO term among the top 5 probable GO terms with accuracy of 86.93%. CONCLUSIONS: The proposed algorithm is the first instance of neural response algorithm being used in the biological domain. The use of HMM profiles along with the secondary structure information to define the neural response gives our method an edge over other available methods on annotation accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/.
Persistent Identifierhttp://hdl.handle.net/10722/163779
ISSN
2015 Impact Factor: 2.213
2015 SCImago Journal Rankings: 1.493
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYalamanchili, HKen_US
dc.contributor.authorXiao, QWen_US
dc.contributor.authorWang, Jen_US
dc.date.accessioned2012-09-20T07:51:35Z-
dc.date.available2012-09-20T07:51:35Z-
dc.date.issued2012en_US
dc.identifier.citationThe 5th IEEE International Conference on Computational Systems Biology (ISB 2011), Zhuhai, China. 2-4 September 2011. In BMC Systems Biology, 2012, v. 6 n. suppl. 1, article no. S19en_US
dc.identifier.issn1752-0509-
dc.identifier.urihttp://hdl.handle.net/10722/163779-
dc.description.abstractBACKGROUND: Large amounts of data are being generated by high-throughput genome sequencing methods. But the rate of the experimental functional characterization 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. Adding to this, the lack of annotation specificity advocates the need to improve automated protein function prediction. RESULTS: We designed a novel automated protein functional assignment method based on the neural response algorithm, which simulates the neuronal behavior of the visual cortex in the human brain. Firstly, we predict the most similar target protein for a given query protein and thereby assign its GO term to the query sequence. When assessed on test set, our method ranked the actual leaf GO term among the top 5 probable GO terms with accuracy of 86.93%. CONCLUSIONS: The proposed algorithm is the first instance of neural response algorithm being used in the biological domain. The use of HMM profiles along with the secondary structure information to define the neural response gives our method an edge over other available methods on annotation accuracy. Results of the 5-fold cross validation and the comparison with PFP and FFPred servers indicate the prominent performance by our method. The program, the dataset, and help files are available at http://www.jjwanglab.org/NRProF/.-
dc.languageengen_US
dc.publisherBioMed Central Ltd.. The Journal's web site is located at http://www.biomedcentral.com/bmcsystbiol/-
dc.relation.ispartofBMC Systems Biologyen_US
dc.rightsBMC Systems Biology. Copyright © BioMed Central Ltd.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleA novel neural response algorithm for protein function predictionen_US
dc.typeConference_Paperen_US
dc.identifier.emailWang, J: junwen@hku.hken_US
dc.identifier.authorityWang, JJ=rp00280en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1186/1752-0509-6-S1-S19-
dc.identifier.pmid23046521-
dc.identifier.pmcidPMC3403322-
dc.identifier.hkuros208298en_US
dc.identifier.volume6en_US
dc.identifier.issuesuppl. 1, article no. S19en_US
dc.identifier.isiWOS:000306568400019-
dc.publisher.placeUnited Kingdom-

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