title: Prediction of protein-protein interactions with clustered amino acids and weighted sparse representation.
abstract: With the completion of the Human Genome Project, bioscience has entered into the era of the genome and proteome. Therefore, protein-protein interactions (PPIs) research is becoming more and more important. Life activities and the protein-protein interactions are inseparable, such as DNA synthesis, gene transcription activation, protein translation, etc. Though many methods based on biological experiments and machine learning have been proposed, they all spent a long time to learn and obtained an imprecise accuracy. How to efficiently and accurately predict PPIs is still a big challenge. To take up such a challenge, we developed a new predictor by incorporating the reduced amino acid alphabet (RAAA) information into the general form of pseudo-amino acid composition (PseAAC) and with the weighted sparse representation-based classification (WSRC). The remarkable advantages of introducing the reduced amino acid alphabet is being able to avoid the notorious dimensionality disaster or overfitting problem in statistical prediction. Additionally, experiments have proven that our method achieved good performance in both a low- and high-dimensional feature space. Among all of the experiments performed on the PPIs data of Saccharomyces cerevisiae, the best one achieved 90.91% accuracy, 94.17% sensitivity, 87.22% precision and a 83.43% Matthews correlation coefficient (MCC) value. In order to evaluate the prediction ability of our method, extensive experiments are performed to compare with the state-of-the-art technique, support vector machine (SVM). The achieved results show that the proposed approach is very promising for predicting PPIs, and it can be a helpful supplement for PPIs prediction.
authors.affiliation: Shenzhen Graduate School, Harbin Institute of Technology, HIT Campus of University Town of Shenzhen, Shenzhen 518055, China. charwinghuang@gmail.com.
authors.forename: Qiaoying
authors.initials: Q
authors.lastname: Huang
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. zhuhong.you@polyu.edu.hk.
authors.forename: Zhuhong
authors.initials: Z
authors.lastname: You
authors.affiliation: Shenzhen Graduate School, Harbin Institute of Technology, HIT Campus of University Town of Shenzhen, Shenzhen 518055, China. zhangxiaofeng@gmail.com.
authors.forename: Xiaofeng
authors.initials: X
authors.lastname: Zhang
authors.affiliation: School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China. yzhou@cumt.edu.cn.
authors.forename: Yong
authors.initials: Y
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D000596:Amino Acids / Q000378:metabolism*; D016000:Cluster Analysis; D016480:Helicobacter pylori / Q000378:metabolism; D006801:Humans; D010455:Peptides / Q000737:chemistry / Q000378:metabolism; D025941:Protein Interaction Mapping / Q000379:methods*; D012372:ROC Curve; D015203:Reproducibility of Results; D060388:Support Vector Machine
chemical_list: D000596:Amino Acids; D010455:Peptides
keywords: protein–protein interactions; reduced amino acid alphabet; weighted sparse representation-based classification