Identification of DNA–protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information
AbstractDNA–protein interactions appear as pivotal roles in diverse biological procedures and are paramount for cell metabolism, while identifying them with computational means is a kind of prudent scenario in depleting in vitro and in vivo experimental charging. A variety of state-of-the-art investigations have been elucidated to improve the accuracy of the DNA–protein binding sites prediction. Nevertheless, structure-based approaches are limited under the condition without 3D information, and the predictive validity is still refinable. In this essay, we address a kind of competitive method called Multi-scale Local Average Blocks (MLAB) algorithm to solve this issue. Different from structure-based routes, MLAB exploits a strategy that not only extracts local evolutionary information from primary sequences, but also using predicts solvent accessibility. Moreover, the construction about predictors of DNA–protein binding sites wields an ensemble weighted sparse representation model with random under-sampling. To evaluate the performance of MLAB, we conduct comprehensive experiments of DNA–protein binding sites prediction. MLAB gives
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Shen, C.; Ding, Y.; Tang, J.; Song, J.; Guo, F. Identification of DNA–protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information. Molecules 2017, 22, 2079.
Shen C, Ding Y, Tang J, Song J, Guo F. Identification of DNA–protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information. Molecules. 2017; 22(12):2079.Chicago/Turabian Style
Shen, Cong; Ding, Yijie; Tang, Jijun; Song, Jian; Guo, Fei. 2017. "Identification of DNA–protein Binding Sites through Multi-Scale Local Average Blocks on Sequence Information." Molecules 22, no. 12: 2079.
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