Ensemble classifier

Improved method for predicting protein fold patterns with ensemble classifiers

W. Chen, Liu, X., Huang, Y., Jiang, Y., Zou, Q., and Lin, C., Improved method for predicting protein fold patterns with ensemble classifiers, vol. 11, pp. 174-181, 2012.

Protein folding is recognized as a critical problem in the field of biophysics in the 21st century. Predicting protein-folding patterns is challenging due to the complex structure of proteins. In an attempt to solve this problem, we employed ensemble classifiers to improve prediction accuracy. In our experiments, 188-dimensional features were extracted based on the composition and physical-chemical property of proteins and 20-dimensional features were selected using a coupled position-specific scoring matrix.

Mining SNPs from EST sequences using filters and ensemble classifiers

J. Wang, Zou, Q., and Guo, M. Z., Mining SNPs from EST sequences using filters and ensemble classifiers, vol. 9, pp. 820-834, 2010.

Abundant single nucleotide polymorphisms (SNPs) provide the most complete information for genome-wide association studies. However, due to the bottleneck of manual discovery of putative SNPs and the inaccessibility of the original sequencing reads, it is essential to develop a more efficient and accurate computational method for automated SNP detection. We propose a novel computational method to rapidly find true SNPs in public-available EST (expressed sequence tag) databases; this method is implemented as SNPDigger. EST sequences are clustered and aligned.

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