Please use this identifier to cite or link to this item:
|Title:||Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties|
Statistical learning method
Support vector machine
|Citation:||Cui, J., Han, L.Y., Li, H., Ung, C.Y., Tang, Z.Q., Zheng, C.J., Cao, Z.W., Chen, Y.Z. (2007-01). Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties. Molecular Immunology 44 (4) : 514-520. ScholarBank@NUS Repository. https://doi.org/10.1016/j.molimm.2006.02.010|
|Abstract:||Background: Computational methods have been developed for predicting allergen proteins from sequence segments that show identity, homology, or motif match to a known allergen. These methods achieve good prediction accuracies, but are less effective for novel proteins with no similarity to any known allergen. Methods: This work tests the feasibility of using a statistical learning method, support vector machines, as such a method. The prediction system is trained and tested by using 1005 allergen proteins from the Allergome database and 22,469 non-allergen proteins from 7871 Pfam families. Results: Testing results by an independent set of 229 allergen and 6717 non-allergen proteins from 7871 Pfam families show that 93.0% and 99.9% of these are correctly predicted, which are comparable to the best results of other methods. Of the 18 novel allergen proteins non-homologous to any other proteins in the Swissprot database, 88.9% is correctly predicted. A further screening of 168,128 proteins in the Swissprot database finds that 2.9% of the proteins are predicted as allergen proteins, which is consistent with the estimated numbers from motif-based methods. Conclusions: Our study suggests that SVM is a potentially useful method for predicting allergen proteins and it has certain capability for predicting novel allergen proteins. Our software can be accessed at http://jing.cz3.nus.edu.sg/cgi-bin/APPEL. © 2006 Elsevier Ltd. All rights reserved.|
|Source Title:||Molecular Immunology|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 22, 2018
WEB OF SCIENCETM
checked on May 15, 2018
checked on May 25, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.