Predicting Subcellular Localization Of Multiple Sites Proteins

Accurate classification on protein subcellular localization plays an important role in Bioinformatics. An increasingly evidences demonstrate that a variety of classification methods have been employed in this field. This research adopts feature fusion method to extract the information of the protein subcellular. Several types of features are employed in this protein coding method, which include amino acid index distribution, the stereo-chemical properties of amino acids and the information for local sequence of amino acids. On base of this feature combination method, flexible neutral tree (FNT) is employed to predict multiplex protein subcellular locations. The overall accuracy rate of using flexible neutral tree as prediction algorithm may reach a better result.