Please use this identifier to cite or link to this item: https://doi.org/10.1002/pmic.200500938
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dc.titleRecent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity.
dc.contributor.authorHan, L.
dc.contributor.authorCui, J.
dc.contributor.authorLin, H.
dc.contributor.authorJi, Z.
dc.contributor.authorCao, Z.
dc.contributor.authorLi, Y.
dc.contributor.authorChen, Y.
dc.date.accessioned2014-05-19T02:57:28Z
dc.date.available2014-05-19T02:57:28Z
dc.date.issued2006-07
dc.identifier.citationHan, L., Cui, J., Lin, H., Ji, Z., Cao, Z., Li, Y., Chen, Y. (2006-07). Recent progresses in the application of machine learning approach for predicting protein functional class independent of sequence similarity.. Proteomics 6 (14) : 4023-4037. ScholarBank@NUS Repository. https://doi.org/10.1002/pmic.200500938
dc.identifier.issn16159853
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/53354
dc.description.abstractProtein sequence contains clues to its function. Functional prediction from sequence presents a challenge particularly for proteins that have low or no sequence similarity to proteins of known function. Recently, machine learning methods have been explored for predicting functional class of proteins from sequence-derived properties independent of sequence similarity, which showed promising potential for low- and non-homologous proteins. These methods can thus be explored as potential tools to complement alignment- and clustering-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using machine learning methods for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented, which need to be interpreted with caution as they are dependent on such factors as datasets used and choice of parameters.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1002/pmic.200500938
dc.sourceScopus
dc.typeReview
dc.contributor.departmentBIOLOGICAL SCIENCES
dc.contributor.departmentPHARMACY
dc.contributor.departmentCOMPUTATIONAL SCIENCE
dc.description.doi10.1002/pmic.200500938
dc.description.sourcetitleProteomics
dc.description.volume6
dc.description.issue14
dc.description.page4023-4037
dc.identifier.isiut000239358600006
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