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Inside Cover, Volume 34, Issue 11 Journal of Computational Chemistry 34

Version of Record online: 20 MAR 2013 | DOI: 10.1002/jcc.23290

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TargetATPsite, a new method based on residue evolution image sparse representation and classifier ensemble is developed for predicting ATP-binding, sites from primary sequences, as presented by Dong-Jun Yu, Jun Hu, Yan Huang, Hong-Bin Shen, Yong Qi, Zhen-Min Tang, and Jing-Yu Yang on page 974. The high performance of TargetATPsite originates from the good discriminative capability of the new image sparse representation feature and the power of the modified AdaBoost classifier ensemble. TargetATPsite also features the capability of further identifying the binding pockets from the predicted binding residues through a spatial clustering algorithm.

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