title: A Novel Feature Extraction Method with Feature Selection to Identify Golgi-Resident Protein Types from Imbalanced Data.
abstract: The Golgi Apparatus (GA) is a major collection and dispatch station for numerous proteins destined for secretion, plasma membranes and lysosomes. The dysfunction of GA proteins can result in neurodegenerative diseases. Therefore, accurate identification of protein subGolgi localizations may assist in drug development and understanding the mechanisms of the GA involved in various cellular processes. In this paper, a new computational method is proposed for identifying cis-Golgi proteins from trans-Golgi proteins. Based on the concept of Common Spatial Patterns (CSP), a novel feature extraction technique is developed to extract evolutionary information from protein sequences. To deal with the imbalanced benchmark dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is adopted. A feature selection method called Random Forest-Recursive Feature Elimination (RF-RFE) is employed to search the optimal features from the CSP based features and g-gap dipeptide composition. Based on the optimal features, a Random Forest (RF) module is used to distinguish cis-Golgi proteins from trans-Golgi proteins. Through the jackknife cross-validation, the proposed method achieves a promising performance with a sensitivity of 0.889, a specificity of 0.880, an accuracy of 0.885, and a Matthew's Correlation Coefficient (MCC) of 0.765, which remarkably outperforms previous methods. Moreover, when tested on a common independent dataset, our method also achieves a significantly improved performance. These results highlight the promising performance of the proposed method to identify Golgi-resident protein types. Furthermore, the CSP based feature extraction method may provide guidelines for protein function predictions.
authors: School of Control Science and Engineering, Shandong University, Jinan 250061, China. runtao-sd@163.com.
authors: Runtao
authors: R
authors: Yang
authors: School of Control Science and Engineering, Shandong University, Jinan 250061, China. cjzhang@sdu.edu.cn.|School of Mechanical, Electrical and Information Engineering, Shandong University atWeihai, Weihai 264209, China. cjzhang@sdu.edu.cn.
authors: Chengjin
authors: C
authors: Zhang
authors: School of Control Science and Engineering, Shandong University, Jinan 250061, China. gaorui@sdu.edu.cn.
authors: Rui
authors: R
authors: Gao
authors: School of Control Science and Engineering, Shandong University, Jinan 250061, China. zlnabc2010@163.com.
authors: Lina
authors: L
authors: Zhang
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry; D000818:Animals; D005075:Biological Evolution; D019295:Computational Biology / Q000379:methods; D030562:Databases, Protein; D004198:Disease Susceptibility; D006056:Golgi Apparatus / Q000378:metabolism*; D006801:Humans; D010455:Peptides / Q000737:chemistry; D011506:Proteins / Q000737:chemistry / Q000378:metabolism*; D012372:ROC Curve; D015203:Reproducibility of Results; D049490:Systems Biology / Q000379:methods
chemical_list: D000596:Amino Acids; D010455:Peptides; D011506:Proteins
keywords: common spatial patterns; golgi apparatus proteins; random forest; recursive feature elimination; synthetic minority over-sampling technique