Document Details

Document Type : Article In Journal 
Document Title :
iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets
iPPBS-Opt: A Sequence-Based Ensemble Classifier for Identifying Protein-Protein Binding Sites by Optimizing Imbalanced Training Datasets
 
Document Language : English 
Abstract : Knowledge of protein-protein interactions and their binding sites is indispensable for in-depth understanding of the networks in living cells. With the avalanche of protein sequences generated in the postgenomic age, it is critical to develop computational methods for identifying in a timely fashion the protein-protein binding sites (PPBSs) based on the sequence information alone because the information obtained by this way can be used for both biomedical research and drug development. To address such a challenge, we have proposed a new predictor, called iPPBS-Opt, in which we have used: (1) the K-Nearest Neighbors Cleaning (KNNC) and Inserting Hypothetical Training Samples (IHTS) treatments to optimize the training dataset; (2) the ensemble voting approach to select the most relevant features; and (3) the stationary wavelet transform to formulate the statistical samples. Cross-validation tests by targeting the experiment-confirmed results have demonstrated that the new predictor is very promising, implying that the aforementioned practices are indeed very effective. Particularly, the approach of using the wavelets to express protein/peptide sequences might be the key in grasping the problem's essence, fully consistent with the findings that many important biological functions of proteins can be elucidated with their low-frequency internal motions. To maximize the convenience of most experimental scientists, we have provided a step-by-step guide on how to use the predictor's web server (http://www.jci-bioinfo.cn/iPPBS-Opt) to get the desired results without the need to go through the complicated mathematical equations involved. 
ISSN : 1420-3049 
Journal Name : Molecules 
Volume : 21 
Issue Number : 1 
Publishing Year : 1437 AH
2016 AD 
Article Type : Article 
Added Date : Monday, May 2, 2016 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
Jianhua JiaJia, Jianhua Investigator jjia@gordonlifescience.org
Zi LiuLiu, Zi Researcher liuzi189836@163.com
Xuan XiaoXiao, Xuan Researcher xxiao@gordonlifescience.org
Bingxiang LiuLiu, Bingxiang Researcher lbx1966@163.com
Kuo-Chen ChouChou, Kuo-Chen Researcher kcchou@gordonlifescience.org

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