Protein Sub-Nuclear Localization Based on Effective Fusion Representations and Dimension Reduction Algorithm LDA

International Journal of Molecular Sciences

Shunfang Wang¹  Shuhui Liu² 

[1] School of Information Science and Engineering, Yunnan University, Kunming 650504, China

关键词: protein sub-nuclear localization;    DipPSSM;    PseAAPSSM;    linear discriminant analysis;    KNN classifier;   

DOI  :  10.3390/ijms161226237

来源: mdpi

【 摘 要 】

An effective representation of a protein sequence plays a crucial role in protein sub-nuclear localization. The existing representations, such as dipeptide composition (DipC), pseudo-amino acid composition (PseAAC) and position specific scoring matrix (PSSM), are insufficient to represent protein sequence due to their single perspectives. Thus, this paper proposes two fusion feature representations of DipPSSM and PseAAPSSM to integrate PSSM with DipC and PseAAC, respectively. When constructing each fusion representation, we introduce the balance factors to value the importance of its components. The optimal values of the balance factors are sought by genetic algorithm. Due to the high dimensionality of the proposed representations, linear discriminant analysis (LDA) is used to find its important low dimensional structure, which is essential for classification and location prediction. The numerical experiments on two public datasets with KNN classifier and cross-validation tests showed that in terms of the common indexes of sensitivity, specificity, accuracy and MCC, the proposed fusing representations outperform the traditional representations in protein sub-nuclear localization, and the representation treated by LDA outperforms the untreated one.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.