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Bioinformatics Data Mining Using a Novel Boosting Algorithm

Yan Wang, Ben Cang Liu


In bioinformatics field Information of subcellular locations of proteins is important for in-depth studies of molecular biology, because protein has to be located in its proper position in a cell to perform its biological functions. Therefore, predicting protein location is an important and challenging task. In this paper, a computational method based AdaBoost.M1 algorithm to identify protein subcellular location. Compared with existing methods for predicting protein subcellular location, The AdaBoost.ME is outperformed than other methods used by previous researchers and can be used to identify proteins locations. From the result, we can draw a conclusion that the accuracy of this method and can make the prediction into practice.


Bioinformatics Data Mining, novel boosting algorithm, multi-class, pseudo amino acid composition.

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