A new algorithm based on complex wavelet transform for protein sequence classification

2012 ◽  
Author(s):  
Li Liu ◽  
Cheng Zhang ◽  
Haojie Wang
Keyword(s):  
Wavelet Transform ◽  
Protein Sequence ◽  
Sequence Classification ◽  
Complex Wavelet Transform ◽  
Protein Sequence Classification ◽  
Complex Wavelet

scholarly journals Prediction of Drug–Target Interactions by Combining Dual-Tree Complex Wavelet Transform with Ensemble Learning Method

Molecules ◽  
2021 ◽  
Vol 26 (17) ◽  
pp. 5359
Author(s):  
Jie Pan ◽  
Li-Ping Li ◽  
Zhu-Hong You ◽  
Chang-Qing Yu ◽  
Zhong-Hao Ren ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Drug Discovery ◽  
Protein Sequence ◽  
Drug Target ◽  
New Drugs ◽  
Evolutionary Information ◽  
Support Vector ◽  
Sequence Information ◽  
Complex Wavelet Transform ◽  
Complex Wavelet

Identification of drug–target interactions (DTIs) is vital for drug discovery. However, traditional biological approaches have some unavoidable shortcomings, such as being time consuming and expensive. Therefore, there is an urgent need to develop novel and effective computational methods to predict DTIs in order to shorten the development cycles of new drugs. In this study, we present a novel computational approach to identify DTIs, which uses protein sequence information and the dual-tree complex wavelet transform (DTCWT). More specifically, a position-specific scoring matrix (PSSM) was performed on the target protein sequence to obtain its evolutionary information. Then, DTCWT was used to extract representative features from the PSSM, which were then combined with the drug fingerprint features to form the feature descriptors. Finally, these descriptors were sent to the Rotation Forest (RoF) model for classification. A 5-fold cross validation (CV) was adopted on four datasets (Enzyme, Ion Channel, GPCRs (G-protein-coupled receptors), and NRs (Nuclear Receptors)) to validate the proposed model; our method yielded high average accuracies of 89.21%, 85.49%, 81.02%, and 74.44%, respectively. To further verify the performance of our model, we compared the RoF classifier with two state-of-the-art algorithms: the support vector machine (SVM) and the k-nearest neighbor (KNN) classifier. We also compared it with some other published methods. Moreover, the prediction results for the independent dataset further indicated that our method is effective for predicting potential DTIs. Thus, we believe that our method is suitable for facilitating drug discovery and development.

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