Classification of Riboswitch Families Using Block Location-Based Feature Extraction (BLBFE) Method

Message:
Article Type:
Research/Original Article (دارای رتبه معتبر)
Abstract:
Purpose

Riboswitches are special non-coding sequences usually located in mRNAs’ un-translated regions and regulate gene expression and consequently cellular function. Furthermore, their interaction with antibiotics has been recently implicated. This raises more interest in development of bioinformatics tools for riboswitch studies. Herein, we describe the development and employment of novel block location-based feature extraction (BLBFE) method for classification of riboswitches.

Methods

We have already developed and reported a sequential block finding (SBF) algorithm which, without operating alignment methods, identifies family specific sequential blocks for riboswitch families. Herein, we employed this algorithm for 7 riboswitch families including lysine, cobalamin, glycine, SAM-alpha, SAM-IV, cyclic-di-GMP-I and SAH. Then the study was extended toward implementation of BLBFE method for feature extraction. The outcome features were applied in various classifiers including linear discriminant analysis (LDA), probabilistic neural network (PNN), decision tree and k-nearest neighbors (KNN) classifiers for classification of the riboswitch families. The performance of the classifiers was investigated according to performance measures such as correct classification rate (CCR), accuracy, sensitivity, specificity and f-score.

Results

As a result, average CCR for classification of riboswitches was 87.87%. Furthermore, application of BLBFE method in 4 classifiers displayed average accuracies of 93.98% to 96.1%, average sensitivities of 76.76% to 83.61%, average specificities of 96.53% to 97.69% and average f-scores of 74.9% to 81.91%.

Conclusion

Our results approved that the proposed method of feature extraction; i.e. BLBFE method; can be successfully used for classification and discrimination of the riboswitch families with high CCR, accuracy, sensitivity, specificity and f-score values.

Language:
English
Published:
Advanced Pharmaceutical Bulletin, Volume:10 Issue: 1, Jan 2020
Pages:
97 to 105
https://www.magiran.com/p2073731  
سامانه نویسندگان
  • Corresponding Author (2)
    Moosa Shamsi
    Professor Faculty of Biomedical Engineering, Sahand University of Technology, Sahand University Of Technology, Tabriz, Iran
    Shamsi، Moosa
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