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|Title:||Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness||Authors:||Han, L.Y.
|Issue Date:||Apr-2007||Citation:||Han, L.Y., Zheng, C.J., Xie, B., Jia, J., Ma, X.H., Zhu, F., Lin, H.H., Chen, X., Chen, Y.Z. (2007-04). Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness. Drug Discovery Today 12 (7-8) : 304-313. ScholarBank@NUS Repository. https://doi.org/10.1016/j.drudis.2007.02.015||Abstract:||Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets. © 2007 Elsevier Ltd. All rights reserved.||Source Title:||Drug Discovery Today||URI:||http://scholarbank.nus.edu.sg/handle/10635/102552||ISSN:||13596446||DOI:||10.1016/j.drudis.2007.02.015|
|Appears in Collections:||Staff Publications|
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